Showing papers in "Environmental Earth Sciences in 2016"

Relationship between water-conservation behavior and water education in Guangzhou, China

Abstract: Water conservation is critical under the current state of climate change and population growth; however, water-conservation programs and research in China have generally focused on technological rather than behavioral innovations. This paper focuses on the state of water-conservation behavior and water education in China to assess how education, particularly the 9-year compulsory education program, affects water-conservation behavior. A survey (237 participants) was conducted in Guangzhou, the third largest city in China, to determine the attitudes of citizens towards conserving water. Overall, the following observations were made: (1) although 95 % of the participants were aware of water conservation, only 42 % recognized that it is urgently needed; (2) water-conservation actions lag behind water-conservation awareness, and only 19 % of the participants were willing to perform more than five actions, including daily water reuse and conservation, whereas 48 % of the participants performed less than two actions; (3) additional education will result in improved water-conservation behavior; (4) more than half of the participants who had graduated from primary and secondary schools showed poor water-conservation behavior; and (5) water-conservation education in the 9-year compulsory education program was extremely rare (representing 0.2–1.4 % of the curriculum) and only included in four compulsory courses. From these observations, it was concluded that water education seriously lags behind the economic development of Guangzhou. Water and environmental education should be emphasized in the 9-year compulsory education curriculum because this program has a relatively large number of students in China.

Processes and behaviors of block topple avalanches resulting from carbonate slope failures due to underground mining

Abstract: Major rock landslides are the main type of geological hazards in the mountainous areas of southwestern China, in which failure modes and behaviors are compounded. In this study, field surveys, centrifuge modelling, and numerical simulations were utilized to analyze the failure process of a steep-dip and layered carbonate slope which was induced by underground goaf. These included gravitational bending, interlayer faulting, topple failure of the upper blocks due to underground mining, shearing through the underlying slide-resistant rocks, and catastrophic avalanches. It was concluded that the bending and compaction of the weak interbedded coal seam had occurred on the slope with the effects of gravity. The support for the overburden rocks was lost following the underground mining activities, which led to bedding separation and the generation of cracks. The upper blocks acted on blocks at the toe, and shear failure at the toe was initiated when the force along the joints exceeded the anti-shear strength, which gradually formed an intact failure surface. Then, a rock slide took place which transformed into a catastrophic rock avalanche. The failure process was validated using a FLAC3D simulation and centrifuge testing.

GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

Abstract: The main objective of this study is to propose and verify a novel ensemble methodology that could improve prediction performances of landslide susceptibility models. The proposed methodology is based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost. According to current literature, these methods have been rarely used for the modeling of rainfall-induced landslides. The corridor of the National Road 32 (Vietnam) was selected as a case study. In the first stage, the landslide inventory map with 262 landslide polygons that occurred during the last 20 years was constructed and then was randomly partitioned into a ratio of 70/30 for training and validating the models. Second, ten landslide conditioning factors were prepared such as slope, aspect, relief amplitude, topographic wetness index, topographic shape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall. The model performance was assessed and compared using the receiver operating characteristic and statistical evaluation measures. Overall, the FT with Bagging model has the highest prediction capability (AUC = 0.917), followed by the FT with MultiBoost model (AUC = 0.910), the FT model (AUC = 0.898), and the FT with AdaBoost model (AUC = 0.882). Compared with those derived from popular methods such as J48 decision trees and artificial neural networks, the performance of the FT with Bagging model is better. Therefore, it can be concluded that the FT with Bagging is promising and could be used as an alternative in landslide susceptibility assessment. The result in this study is useful for land use planning and decision making in landslide prone areas.

Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran

Abstract: In many parts of the world, landslide susceptibility remains inadequately mapped, due to the lack of both data and suitable methods for widespread implementation. Iran is one of those countries with extensive landslide problems, with nearly 4900 large landslides occurring between 1993 and 2007. At the same time landslide susceptibility has not been assessed for the country. Random forest (RF) has recently been shown to be a suitable tool for such mapping. In this study we further coupled the RF method with an evidential belief function (EBF) approach, and tested the suitability for landslide susceptibility mapping for variable terrain and data conditions in the west of Mazandaran Province, northern Iran. Locations of earlier landslides were identified by interpreting aerial photographs and through extensive field surveys. Eleven conditioning factors were used in the RF model. The spatial relationship between landslide occurrence and conditioning factors was then assessed using the data-driven EBF model, and EBF values paired to each map. Finally, the EBF maps were used for running the RF model. Finally, the efficiency of the RF-EBF model was tested using the area under the curve to measure the success and prediction rates of the incorporated data. This resulted in a success rate of 85.2 %, and a prediction rate of 81.8 %. The most important conditioning factors identified were lithology, altitude, distance from roads, and land use, respectively. Based on the overall assessment, the combined RF and EBF approach was found to be objective and an applicable estimator that improves the predictive accuracy and controls for overfitting, and thus useful for landslide susceptibility mapping at regional scales.

Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models

Abstract: The city of Jeddah (Saudi Arabia) has experienced two catastrophic flash flood events in 2009 and 2011. These flood events had catastrophic effect on human lives and livelihoods around the wadi Muraikh, wadi Qus, wadi Methweb, and wadi Ghulail in which 113 people were dead and with 10,000 houses and 17,000 vehicles were damaged. Thus, a comprehensive flood management is required. The flood management requires information on different aspects such as the hydrological, geotechnical, environmental, social, and economic aspects of flooding. Flood susceptibility mapping for any area helps the decision makers to understand the flood trends and can aid in appropriate planning and flood prevention. In this study, two models were used for the generation of flood susceptibility maps for the Jeddah region. The first model includes bivariate probability analysis (frequency ratio), and the second model uses the multivariate analysis. For the multivariate model, the acquired weights of the FR model were entered into the logistic regression model to evaluate the correlation between flood occurrence and each related factor. This integration will overcome some of the weakness of the logistic regression, and the performance the LR will be enhanced. A flood inventory map was prepared with a total of 127 flood locations. These flood locations were extracted from different sources including field investigation and high-resolution satellite image (IKONOS 1 m). These flood locations were randomly split into two groups, one dataset representing 70 % was used for training the models, and the remaining 30 % was used for models validation. Various independent flood-related factors such as slope, elevation, curvature, geology, landuse, soil drain, and distance from streams were included. The impact of each independent flood-related factors on flooding was evaluated by analyzing each independent factor with the historical flood inventory data. The training and validation datasets were used to evaluate the flood susceptibility maps using the success and the prediction rate methods. The results of the accuracy assessment showed a success rate of 90.4 and 91.6 % and a prediction rate of 89.6 and 91.3 % for FR and ensemble FR and LR models, respectively. In addition, a comparison has been made between real flood events in 2009 and the resultant susceptibility maps. Hence, it is concluded that the FR and ensemble Fr and LR models can provide an acceptable accuracy in the prediction of flood susceptibility in the Saudi Arabia. Our findings indicated that these flood susceptibility maps can assist planners, decision makers, and other agencies to deal with the flood management and planning in the area.

GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

Abstract: This study evaluated and compared groundwater spring potential maps produced with two different models—namely multivariate adaptive regression spline (MARS) and random forest (RF)—using geographic information system (GIS). In total, 234 spring locations were identified in the Boujnord, North Khorasan, Iran and a GIS spring inventory map was prepared. Of these, 176 (70 %) locations were employed to produce spring potential maps (training), while the remaining 58 (30 %) cases were used to validate the model. The explanatory variables used to predict spring location were altitude, slope aspect, slope degree, slope length, topographic wetness index (TWI), plan curvature, profile curvature, land use, lithology, distance to rivers, drainage density, distance to faults, and fault density. Furthermore, the spatial relationships between spring occurrence and explanatory variables were performed using a Certainty Factor (CF) model. For validation, area under a receiver operating characteristics (ROC) curves (AUC) was used. The validation results showed that the AUC for calibration is almost identical (0.79) in both models, while for prediction, the MARS model (73.26 %) performed better than RF (70.98 %) model. These results indicate that the MARS and RF models are good estimators of groundwater spring potential in the study area. These groundwater spring potential maps can be applied to groundwater management and groundwater resource exploration.

The future Aral Sea: hope and despair

Abstract: The Aral Sea in 1960 was a huge brackish water lake (4th in the world in surface area) lying amidst the deserts of Central Asia. The sea supported a major fishery and functioned as a key regional transportation route. Since 1960, the Aral has undergone rapid desiccation and salinization, overwhelmingly the result of unsustainable expansion of irrigation that dried up its two tributary rivers the Amu Darya and Syr Darya and severely damaged their deltas. The desiccation of the Aral Sea has had severe negative impacts, including, among others, the demise of commercial fishing, devastation of the floral and faunal biodiversity of the native ecosystems of the Syr and Amu deltas, and increased frequency and strength of salt/dust storms. However, efforts have been and are being made to partially restore the sea’s hydrology along with its biodiversity, and economic value. The northern part of the Aral has been separated from the southern part by a dike and dam, leading to a level rise and lower salinity. This allowed native fishes to return from the rivers and revitalized the fishing industry. Partial preservation of the Western Basin of the southern Aral Sea may be possible, but these plans need much further environmental and economic analysis. This paper, mainly utilizing hydrologic and other data as input to spreadsheet (Microsoft Excel)-based hydrologic and salinity models, examines the current efforts to restore the Aral and looks at several future scenarios of the Sea. It also delineates the most important lessons of the Aral Sea’s drying.

Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran)

Abstract: The main aim of this paper is to develop a new hybrid method to assess landslide susceptibility mapping (LSM) in neighboring provinces of Alborz Mountains in Iran. In the last centuries, this region has experienced a large number of landslides due to its location on earthquake belt, with high precipitation in some parts and having varied topography. Besides, the largest city of Iran (Tehran), a lot of important infrastructures, congested roads and a large population are located in this region. Therefore, determining the spatial outlines of the regions which are prone to future landslides is a critical issue. To reach this goal, the LSM is provided by applying a hybrid model of statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) in a Geographic Information System. In the first step, landslide inventory map was divided into two groups randomly. These groups are training dataset including 70 % recorded landslides and the remaining 30 % was used to test the model output. The first and second groups are used to determine the weights in model and validation of results, respectively. Then, 12 landslide conditioning factors are selected and categorized into two groups which are continuous numerical and nominal. After that, each factor is classified and the weight of each class is determined using Wi. The outputs of Wi and Wi-ANFIS were employed to determine nominal and continuous numerical data, respectively. In the Wi-ANFIS approach, the calculated weights of each class is allocated to the center of each class, and the rest weights of values are determined by ANFIS which is an artificial algorithm using training data (in this paper, the weights were calculated by Wi) in terms of predicting and interpolating. The results are evaluated using receiver operation curves including success rate curve and predicted rate curve. The validation results of the proposed hybrid method shows that the area under the curve of success rate curve and predicted rate curve are 0.90 and 0.89, respectively, which have been improved in comparison with Wi. The results pr oved that the suggested model applied in this study generated reliable LSM which can be applicable for primary land use planning and infrastructure site selection.

Protected areas may not effectively support conservation of endangered forest plants under climate change

Abstract: Protected areas (PAs) play an important role in the conservation of valuable forest resources, and an increasing number of areas are being designated as PAs worldwide. However, climate change could drive endangered forest plants out of PAs, and impact the function of PAs to conserve endangered forest plants. Hence, it is necessary for conservation biologists to put forward a simple method to evaluate the ability of PAs to conserve endangered forest plants. Here, we studied 61 endangered forest plants from three ecoregions in China. We applied species distribution modeling to project suitable habitats of endangered forest plants, and used geographical information system to compute whether PAs could support the conservation of endangered forest plants. With climate change caused by increasing gas concentration, the overall ability of PAs to support the conservation of endangered forest plants will likely decrease compared to the conservation needs of ecoregions. We found that PAs have varying abilities to conserve endangered forest plants in different ecoregions. For temperate broadleaf mixed forests and tropical and subtropical moist broadleaf forests, we found that climate change will decrease the PAs’ ability to support the conservation of endangered forest plants effectively in the existing forest landscape. In contrast, we found that temperate conifer forests will likely remain effective. Using this information, we proposed the conservation plans for different ecoregions under climate change. For PAs with limited ability to support the conservation of endangered forest plants in an ecoregion, we recommend expanding the areas of forests and PAs based on the suitable habitats of the endangered forest plants. For PAs with stable ability to support the conservation of endangered forest plants in an ecoregion, we recommend expanding the conservation areas in PAs.

A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction

Abstract: This study investigates the applicability of multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM) models for prediction of river flow time series. Monthly river flow time series for period of 1989–2011 of Safakhaneh, Santeh and Polanian hydrometric stations from Zarrinehrud River located in north-western Iran were used. To obtain the best input–output mapping, different input combinations of antecedent monthly river flow and a time index were evaluated. The models results were compared using root mean square errors and the correlation coefficient. A comparison of models indicates that MLP and RBF models predicted better than SVM model for monthly river flow time series. Also the results showed that including a time index within the inputs of the models increases their performance significantly. In addition, the reliability of the models prediction was calculated by an uncertainty estimation. The results indicate that the uncertainty in the SVM model was less than those in the RBF and MLP models for predicting monthly river flow.

Assessment of groundwater quality of Lakshimpur district of Bangladesh using water quality indices, geostatistical methods, and multivariate analysis

Abstract: Groundwater evaluation indices, multivariate statistical techniques, and geostatistical models are applied to assess the source apportionment and spatial variability of groundwater pollutants at the Lakshimpur district of Bangladesh. A total of 70 groundwater samples have been collected from wells (shallow to deep wells, i.e., 10–375 m) from the study area. Groundwater quality index reveals that 50 % of the water samples belong to good-quality water. The degrees of contamination, heavy metal pollution index, and heavy metal evaluation index present diversified results in samples even though they show significant correlations among them. The results of principal component analysis (PCA) show that groundwater quality in the study area mainly has geogenic (weathering and geochemical alteration of source rock) sources followed by anthropogenic source (agrogenic, domestic sewage, etc.). Cluster analysis and correlation matrix also supported the results of PCA. The Gaussian semivariogram models have been tested as the best fit models for most of the water quality indices and PCA components. The results of semivariogram models have shown that most of the variables have weak spatial dependence, indicating agricultural and residential/domestic influences. The spatial distribution maps of water quality parameters have provided a useful and robust visual tool for decision makers toward defining adaptive measures. This study is an implication to show the multiple approaches for quality assessment and spatial variability of groundwater as an effort toward a more effective groundwater quality management.

Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

Abstract: Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches—random forest, boosted regression trees, and Cubist—were examined for the downscaling of AMSR-E soil moisture (25 × 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through cross-validation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moisture.

Groundwater quality assessment using statistical approach and water quality index in Ejisu-Juaben Municipality, Ghana

Abstract: Groundwater represents a significant source of fresh water for drinking purposes and, therefore, preserving its availability and quality is extremely important. The hydrochemical characteristics and quality of groundwater in Ejisu-Juaben Municipality, Ghana have been evaluated based on different indices for assessing groundwater for drinking purposes. A total of 19 groundwater samples were collected and analyzed for major cations and anions using standard methods. The results show that the groundwater parameters were within the permissible limits of the World Health Organization except phosphate. The domination of major ions was in the order of Ca2+ > Mg2+ > NH4 + for cations and Cl− > HCO3 − > SO4 2− > PO4 2− > NO3 − > NO2 − in anions. The hydrochemical analysis suggest that the dominant ions were derived from ion exchange and silicate weathering process. According to Gibbs plot, the predominant samples fall in the rock–water interaction and precipitation dominance field. The R-mode factor analysis shows that the four factors extracted account for 83.9 % of the total variance. Groundwater quality index reveals that the majority of the samples falls under good to excellent category of water, suggesting that the groundwater is suitable for drinking and other domestic uses.

Urban flood hazard assessment in the basin of Athens Metropolitan city, Greece

Abstract: Flood events have often occurred in the metropolitan urban area of Athens, capital of Greece, causing loss of property and in many cases human lives. In this study a flood hazard assessment model for urban areas is examined. The Kifisos and Ilisos Rivers flowing through the plain of Athens was the case study of the present work. The quantitative analysis of the Kifisos and Ilisos Rivers drainage networks was performed to identify flash-flood prone areas. The major factors affecting urban floods were estimated. The slope angle, elevation, distance from open channel streams, distance from totally covered streams, hydro-lithology and land cover of the study area were used. to evaluate these factors the analytical hierarchical process method was applied in a geographical information system. A sensitivity analysis was made to assess the effect of the various factors on the flood hazard map. Three scenarios were developed to examine the effect of uncertainty of the factors’ values to the flood hazard assessment results, leading to the corresponding urban flood hazard assessment maps. The produced map showed that the areas of very high flood hazard are located mostly along the lower reaches of Kifisos and Ilisos Rivers, particularly to the southern and to the western part of the study area. These areas are characterized by lowland morphology, gentle slope, totally covered streams, expansion of impermeable formation and intense urbanization. The uncertainty analysis shows no significant differences on the spatial distribution of the hazard zones. The produced urban flood hazard map proves a satisfactory agreement between the flood hazard zones and the spatial distribution of flood phenomena that have affected the study area in the past 117 years. Furthermore, the comparison between the flood-prone areas that were derived from the geomorphological analysis of the drainage networks and the high flood-hazard zones of the final map indicated reliable results and a high accuracy of the proposed methodology.

Modeling landfill site selection by multi-criteria decision making and fuzzy functions in GIS, case study: Shabestar, Iran

Abstract: Landfilling is the most common method used for disposal of solid waste and selection of suitable landfill for municipal solid waste management is important part of urban planning. The aims of this paper are determining the constraints, criteria for landfill site selection according to present legislations in Iran, zoning potential of Shabestar city for construction of municipal landfill and selection of the most suitable sites. At first, by reviewing of internal and external literature, the criteria which were necessary to achieve the aims of study were determined. Then, conceptual model of landfill site selection steps and process was developed. After that, the required maps were collected from relevant organizations and prepared using geographic information systems (GIS) tool in order to use in the next steps. Finally, all criteria maps were standardized by fuzzy functions and entered in mathematical combination model. In this study, the fuzzy functions in ArcGIS 10 including linear, large, small and Gaussian were used. Weighting of criteria was performed by analytical hierarchy process (AHP) method using expert choice application and weighted linear combination (WLC) was chosen for integration of maps. Preliminary results of zoning showed that nearly 6.2 % of the study area has good suitability for municipal landfill. To select the best site among the candidate sites for Shabestar city, at first the sensitive analysis was performed and then the best site was selected based on required area, soil depth, dominant wind direction and visibility from residential area.

AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County

Abstract: This paper offers an integrated approach to contribute to the process of agricultural land suitability analysis using the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) methods. The paper addresses Cihanbeyli, the largest county in Turkey in terms of area, and focuses on determining sustainable strategies to activate/improve agriculture as a main source of income, thereby improving the economy of the region. The combined AHP and GIS methodology which consists of stages such as structuring AHP hierarchy, describing evaluation criteria, doing pairwise comparisons, and preparing criterion maps and land suitability maps has been applied to identify the areas suitable for irrigated and dry farm agriculture. A comparison of the final land suitability map with current land use has revealed that an area of 294.73 km2 (7.18 %) is suitable for irrigation and an area of 2323.45 km2 (56.77 %) is suitable for dry farm agriculture. Additionally, the analysis clearly shows the necessity of a decrease in irrigated agricultural land and an increase in dry farm agricultural land. The applied AHP and GIS based agricultural land suitability analysis is useful in (1) referring agricultural activities to the areas that have good physical and environmental conditions for agriculture, thus achieving maximum agricultural efficiency in countryside, (2) improving non-agricultural uses in the areas that are unsuitable for agriculture and have low efficiency, (3) avoiding the construction and environmental pressures on suitable farmland, so conducing to better land-use planning decisions.

Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines.

Abstract: The main objective of this study is to investigate the potential application of GIS-based Support Vector Machines (SVM) with four kernel functions, i.e., radial basis function (RBF), polynomial (PL), sigmoid (SIG), and linear (LN) for landslide susceptibility mapping at Luxi city in Jiangxi province, China. At the first stage of the study, a landslide inventory map with 282 landslide locations was identified using aerial photographs, satellite images, and field surveys. Of this, 70 % of the landslides (196 landslide locations) are used as a training dataset and the rest (86 landslide locations) were used as the validation dataset. Then, 15 landslide conditioning factors were prepared, i.e., altitude, aspect, slope, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), plan curvature, profile curvature, distance from river, distance from road, distance from fault, lithology, land use, NDVI, and rainfall. Using these conditioning factors, landslide susceptibility indexes were calculated using SVM with the four kernel functions. Subsequently, the results were exported and plotted in ArcGIS and four landslide susceptibility maps were produced. The four susceptibility maps were validated and compared using the landslide locations and the success rate and prediction rate methods. The validation results showed that success rates for the four SVM models are 82.0 % (RBF), 83.0 % (PL), 45.0 % (SIG), and 70.0 % (LN). The prediction rates for the four SVM models are 81.0 % (RBF), 71.0 % (PL), 40.0 % (SIG), and LN 63.0 % (SIG). The result shows that the RBF-SVM model has the highest overall performance. The produced susceptibility maps may be useful for general land-use planning in landslides.

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

Abstract: The impulsive wave is considered as one of the most notably secondary hazards induced by landslides in reservoir areas. The impulsive wave with considerable wave amplitude is able to cause serious damage to the dam body, shoreline properties and lives. To investigate and predict the wave characteristics, many experimental studies employed the generalized channels rather than the realistic topography. Deviation from the idealized geometries may result in non-negligible effects due to the wave refraction or reflection with complex topography. To consider the topography effect, a prototype scaled experiment was conducted. A series of tests with different collocation of parameters were performed. The experimental results were then summarized to propose empirical equations to predict the maximum wave amplitude, and wave decay in channel direction. The generalized empirical equations can obtain better results for wave features prediction by compared with those derived from the idealized models. Furthermore, a 3D numerical modeling corresponding to the physical experiment was conducted based on the SPH method. The wave characteristics in the sliding and channel directions were investigated in detail including the maximum wave amplitude, wave run-up, wave arrival time and wave crest amplitude decay. The comparison between the simulation and experiment indicates the promising accuracy of the SPH simulation in determining the general features even with complex river topography. Finally, the limitation and applicability of both the experimental and numerical methods in analyzing the practical engineering problems were discussed. Combination of the both methods can benefit the hazard prevention and reduction for landslide generated impulsive waves in reservoir area.

Distribution, correlation, ecological and health risk assessment of heavy metal contamination in surface soils around an industrial area, Hyderabad, India

Abstract: In recent years, much concern has been addressed over the soil contamination with heavy metals due to rapid industrialization and urbanization. The present study was conducted to investigate distribution of potentially hazardous elements (PHEs) (As, Cr, Cu, Ni, Pb and Zn) concentration in soils of Kazipalli, Hyderabad, India. Soil samples from fifty-seven (57) sampling sites were collected from in and around industrial zone and were analysed for their heavy metal contents. Concentrations ranged from 4.4 to 796.3 mg/kg for As, 9.7 to 598.6 mg/kg for Cr, 7.9 to 183.5 mg/kg for Cu, 10.2 to 129.6 mg/kg for Ni, 25.3 to 1830 mg/kg for Pb and 23.8 to 879 mg/kg for Zn. Application of Pearson’s correlation, factor and cluster analysis indicates that heavy metal contamination in soils originates from industrial activities which are of anthropogenic origin. Contamination of soils in the study area was further classified for geoaccumulation index, enrichment factor, contamination factor and contamination degree. The values of pollution index and integrated pollution index indicated that metal pollution levels were in order of As > Pb > Cu > Cr > Zn > Ni. Potential ecological risk indices (PERI, RI) and health risk assessment based on Hazard Quotient, Hazard index and on Average daily doses of individual elements were calculated using exposure parameters for resident population and references from integrated database of USEPA. These results are important for the development of proper management strategies to decrease point and non-point source of pollution by studying different remediation methods.

Investigating the potential influence of biochar and traditional organic amendments on the bioavailability and transfer of Cd in the soil–plant system

Abstract: In this paper biochar (BC) and traditional soil amendments, including press mud (PrM), farm manure (FM), compost (Cmp), poultry manure (PM) and sewage sludge (SS), were evaluated as carbon sources (20 g kg−1 organic carbon) to assess their ability to store soil organic carbon as well as their effects on the bioavailability, transfer and immobilization of Cd in contaminated soil. Wheat (Triticum aestivum L.) was grown on unamended and amended soil (at 2 % organic carbon basis) in a greenhouse experiment and the soil characteristics, AB-DTPA extractable Cd and metal uptake were determined. The influence of the applied organic carbon amendments on the soil properties and accumulation of Cd was evaluated and compared to unamended soil. All of the amendments increased the soil OC significantly with the highest value (1.2 %) with BC followed by PrM (0.98 %) and Cmp (0.86 %). The maximum decrease in the AB-DTPA extractable Cd (43.82 %) was recorded with the BC amendment followed by Cmp (18.16 %), while FM and PrM amendments increased the Cd availability up to 19.92 and 4.45 %, respectively. The uptake of Cd by Triticum aestivum L. was increased with all of the amendments except for BC and Cmp, which showed significant decreases, and the maximum transfer factor was found in SS followed by PrM, PM and FM. The soil organic carbon (OC), cation exchange capacity (CEC) and pH were negatively correlated with the AB-DTPA extractable Cd in post-experiment soil. The results obtained from this experiment suggest that organic carbon from biochar (BC) can be used to increase the soil OC stock, and it is also effective for the in situ immobilization/remediation of Cd, thus improving the physico-chemical properties of soil and leading to increased plant growth.

Hot dry rock geothermal potential of the Xujiaweizi area in Songliao Basin, northeastern China

Abstract: A series of studies was conducted to determine a suitable site for enhanced geothermal systems (EGSs) in mainland China. First, the Xujiaweizi (XJWZ) area in the Songliao Basin in northeastern China was identified to possess several features indicating a huge potential for hot dry rock (HDR) resource development from the aspects of tectonics, geology, geophysics, and geothermics. Then, a hydraulic fracturing model based on the real geological and logging data of well YS-2 and field fracturing experience in this region was established, and the geometric dimension and flow conductivity of the induced fracture were imported into a 3D hydrothermal coupled model established using TOUGH2-EOS1. The electricity generation potential of the fractured reservoir using three horizontal well production patterns is evaluated. Finally, three enhanced methods are proposed and discussed based on the simulation results. Results indicate that HDR resources in the XJWZ area demonstrate a significant potential for development from the aspect of geology. The Yingcheng Formation is selected as the potential target formation. The gel-proppant fracturing method is adopted in consideration of the undeveloped natural fractures in the target formation. The generalized EGS reservoir region possessed a considerable length of 600 m. The maximum production flow rate is determined to be 1 kg/s. The water flow impedance is relatively high during the heat production process for economic exploitation. Such an EGS reservoir has poor electricity generation capacity. A naturally fractured reservoir with a higher temperature should be targeted for electricity generation.

Flood hazard mapping in Jamaica using principal component analysis and logistic regression

Abstract: Jamaica, the third largest island in the Caribbean, has been affected significantly by flooding and flood-related damage. Hence assessing the probability of flooding and susceptibility of a place to flood hazard has become a vital part of planning and development. In addition to heavy rainfall from tropical storms and Atlantic hurricanes, several terrestrial factors play significant roles in flooding, including local geology, geomorphology, hydrology and land-use. In this study, a GIS-based multi-criteria statistical methodology was developed to quantify hazard potential and to map flood characteristics. Fourteen factors potentially responsible for flooding were identified and used as initial input in a hybrid model that combined principal component analysis with logistic regression and frequency distribution analysis. Of these factors, seven explained 65 % of the variation in the data: elevation, slope angle, slope aspect, flow accumulation, a topographic wetness index, proximity to a stream network, and hydro-stratigraphic units. These were used to prepare the island’s first map of flood hazard potential. Hazard potential was classified from very low to very high, nearly one-fifth (19.4 %) of the island was included within high or very high flood hazard zones. Further analysis revealed that areas prone to flooding are often low-lying and flat, or have shallow north- or northwest-facing slopes, are in close proximity to the stream network, and are situated on underlying impermeable lithology. The multi-criteria hybrid approach developed could classify 86.8 % of flood events correctly and produced a satisfactory validation result based on the receiver operating characteristic curve. The statistical method can be easily repeated and refined upon the availability of additional or higher quality data such as a high resolution digital elevation model. Additionally, the approach used in this study can be adopted to evaluate flood hazard in countries with similar characteristics, landscapes and climatic conditions, such as other Caribbean or Pacific Small Island Developing States.

GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China

Abstract: The main purpose of this study was to map landslide susceptibility through the AHP and CF models, using a geographic information system (GIS), for the Baozhong region of Baoji City, China. At first, a landslide inventory map was prepared using technical reports, aerial photographs, and coupling with field surveys. A total of 79 landslides were mapped, out of which 55 (70 %) were randomly selected for building landslide susceptibility models, while the rest 24 landslides (30 %) were applied for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using the AHP and CF models. Finally, the validation of landslide susceptibility map was accomplished with areas under the curve (AUC) and the Seed Cell Area Index (SCAI). The AUC plot estimation results indicated that the susceptibility map applying CF model has a higher prediction accuracy of 81.43 % than the accuracy of 75.97 % applying AHP model. Similarly, the validation results also showed that the success rate of the CF model was 85.93 %, while the success rate was 77.80 % for the AHP model. According to the validation results of the AUC evaluation, the map produced by CF model behaves better performance. Furthermore, the validation results using the SCAI also indicated that the CF model has a higher predication accuracy than the AHP model. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation.

Nitrate contamination of groundwater in the western Po Plain (Italy): the effects of groundwater and surface water interactions

Abstract: This study aims to investigate the physical and chemical effects of interactions between groundwater and surface water (GW–SW)—particularly in streams—on nitrate contamination. The effects of GW–SW interactions are briefly reviewed, with a particular emphasis on processes and environments that influence increases or decreases in nitrate concentration. Then, this paper analyses nitrate concentrations in groundwater and surface water in the western Po plain (Northwestern Italy); this analysis includes the nitrate concentration profiles across the shallow aquifer and intersecting the main streams on the plain. The investigation highlights how the concentration trends are similar, even when nitrate levels in rivers and groundwater are not comparable. The maximum nitrate concentrations in the surface water were generally measured in areas with high-nitrate levels in groundwater. An analysis of the nitrate concentration profiles highlighted the mutual influences of GW–SW. The most important streams on the plain (the Po River and Stura di Demonte River), both of them gaining streams, seem to reduce the nitrate concentrations of groundwater at a study scale. The proposed conceptual model indicates how the near-stream environment (the riparian zone, wetlands, hyporheic zone and shallow organic-rich soils in the near-stream environment) and the groundwater flow systems in shallow and deep aquifers, from the recharge zone to the streams, could dramatically affect the nitrate concentrations.

Meteorological drought in Bangladesh: assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data

Abstract: This study focused on assessing and analysing meteorological drought characteristics of Bangladesh based on rainfall, standardized precipitation index (SPI) and geographic information system (GIS). SPI and monthly rainfall time series dataset for the period of 1971–2010 were used to define the drought years and severity. GIS techniques, along with inverse distance weighted interpolation, were used to determine the spatial pattern of drought. Drought occurrences with severity were analysed based on 3-month (SPI-3 January and SPI-3 April) and 6-month (SPI-6 April) time scales. Drought occurrence maps were generated in GIS environment by summarizing the percentage of drought occurrence for each category and for each time scale. For drought hazard mapping, a drought hazard index was computed from 34 observation stations using analytical hierarchy process, weighted sum method and drought occurrence with different severities at different time scales. Afterwards, index values were interpolated and classified into four hazard levels viz. low, moderate, high and very high. This study pointed out that 1972, 1978, 1981, 1982, 1995, 1997, 1999, 2004, 2006 and 2010 were the most drought-affected years since 1971, and 1972, 1978, 1982, 1995, 1999 and 2006 were the worst drought years based on severity. In the last two decades, Bangladesh had high numbers of severe and extreme droughts. Drought occurrences showed that during the study period, comparatively moderate drought occurred more frequently than severe and extreme. The time scale SPI-3 January was more prone to severe drought occurrence while the time scale SPI-3 April was more prone to extreme drought occurrence. The northern, north-western, western, south-western and central parts were the most drought-prone areas of the country in terms of occurrence and severity. Low annual and seasonal rainfall, high variability in rainfall and climate change impacts, and particularly increased maximum temperatures greatly influence droughts in Bangladesh. On the other hand, drought hazard maps of SPI-3 January, SPI-3 April and SPI-6 April showed that high and very high hazardous areas were located in the north-west, west and south-west parts of the country. The Rajshahi, Dinajpur, Rangpur, Bogra, Kushtia were the most drought-prone districts (under very high hazard zone) of the country. The districts Jamalpur, Pabna, Jessore, Khulna, Mymensingh and Tangail were also identified as drought hazardous districts (under high hazard zone). Thus, these parts of the country require urgent intervention on a priority basis to mitigate drought impacts.

The new “Silk Road Economic Belt” as a threat to the sustainable management of Central Asia’s transboundary water resources

Abstract: Central Asia is well known for its history of water mismanagement. The rapid, catastrophic demise of the Aral Sea is testament to the unsustainable water diversion practices introduced by the Soviet Union in the 1960s and the failure of the five sovereign nations, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan and Turkmenistan, that inherited responsibility for Central Asia’s ailing resources, to develop the types of program necessary for the sustainable management of what had become a shared “transboundary” water resource. Although nearly 25 years have passed since the break-up of the Soviet Union, rivalry and deep mistrust between the guardian nations of Central Asia’s water resources remains a serious impediment to achieving the level of cooperation necessary for constructive, water management and decision-making. This is a grave concern given the anticipated impacts of climate change and natural population growth on water in the region. For many Asians, the recently proposed new “Silk Road Economic Belt” is viewed as an immense opportunity to bring wealth and prosperity to some of the poorest regions of China and Central Asia. However, given Central Asia’s appalling record of water management, there is little confidence that the project’s water needs can be adequately met. In effect, the new “Silk Road Economic Belt” and the rapid growth it will bring to the region, represents a serious long-term threat to the sustainable management of Central Asia’s transboundary water resources. The fundamental concern is that the project may place too great a burden on a water management system in Central Asia that is seriously dysfunctional and shows no sign of improvement. Central Asian countries need to recognise that the economic success of the “Silk Road Economic Belt” hinges on their ability to develop programs that can ensure the region’s water resources are managed in a sound and sustainable manner. This will be a difficult challenge and will require cooperation amongst the countries of Central Asia that goes far beyond what currently seems possible. Major reforms are necessary and external pressures from neighbouring Russia and China are likely required to make this happen. It is also essential that the project be supported by sound science and good hydrological data, both of which are seriously lacking in the region. There will be a need to invest in scientific research in the relevant fields. With judicious planning, good science and a commitment amongst the nations of Central Asia to create a shared vision and collaborate towards a common goal, the “New Silk Road” can be developed both beneficially and sustainably.

Recent changes in the active layer thickness across the northern hemisphere

Abstract: To better understand the ecological and hydrological responses to climatic and cryospheric changes, the spatiotemporal variations in the active layer thickness (ALT) need to be scrupulously studied. Based on more than 230 sites from the circumpolar active layer monitoring network, the spatiotemporal characteristics of the ALT across the northern hemisphere during 1990–2015 were investigated. Results indicate that the ALT exhibits substantial spatial variations across the northern hemisphere, ranging from approximately 30 cm in the arctic and subarctic regions to greater than 10 m in the mountainous permafrost regions at mid-latitudes. Regional averages of ALT are 48 cm in Alaska, 93 cm in Canada, 164 cm in the Nordic countries (including Greenland and Svalbard) and Switzerland, 330 cm in Mongolia, 476 cm in Kazakhstan, and 230 cm on the Qinghai-Tibetan Plateau (QTP), respectively. In Russia, the regional averages of ALT in European North, West Siberia, Central Siberia, Northeast Siberia, Chukotka, and Kamchatka are 110, 92, 69, 61, 53 and 60 cm, respectively. Increasing trends of ALT were not uniformly present in the observational records. Significant changes in the ALT were observed at 73 sites, approximately 43.2 % of the investigated 169 sites that are available for statistical analysis. Less than 25 % Alaskan sites and approximately 33 % Canadian sites showed significant increase in the ALT. On the QTP, almost all the sites showed significant ALT increases. Insignificant increase and even decrease in the ALT were observed in some parts of the northern hemisphere, e.g., Mongolia, parts of Alaska and Canada. The air and ground temperatures, vegetation, substrate, microreliefs, and soil moisture in particular, play decisive roles in the spatiotemporal variations in the ALT, but the relationships among each other are complicated and await further studies.

A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS

Abstract: This study investigates the application of information value (InV) and logistic regression (LR) models for producing landslide susceptibility maps (LSMs) of the Zigui–Badong area near the Three Gorges Reservoir in China. This area is subject to anthropogenic influences because the reservoir’s water level cyclically fluctuates between 145 and 175 m. In addition, the area suffers from extreme rainfall events due to the local climate and has experienced significant and widespread landslide events in recent years. In this study, a landslide inventory map was initially constructed using field surveys, aerial photographs, and a literature search of historical landslide records. Eight causative factors, including lithology, bedding structure, slope, aspect, elevation, profile curvature, plane curvature, and fractional vegetation cover, were then considered in the generation of LSMs by using the InV and LR models. Finally, the prediction performances of these maps were assessed through receiver operating characteristics (ROC) that utilized both success-rate and prediction-rate curves. The validation results showed that the area under the ROC curve for the InV model was 0.859 for the success-rate curve and 0.865 for prediction-rate curve; these results indicate the InV model surpassed the LR model (0.742 for success-rate curve and 0.740 for prediction-rate curve). Overall, the two models provided nearly similar results. The results of this study show that landslide susceptibility mapping in the Zigui–Badong area is viable with both approaches.

Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers

Abstract: In the present research, neuro-fuzzy-based group method of data handling (NF-GMDH) has been applied to evaluate the longitudinal dispersion coefficient in rivers. The NF-GMDH model has been improved through particle swarm optimization algorithms (PSO). Effective parameters on the longitudinal dispersion coefficient including flow depth, channel width, cross-sectional average velocity, and bed shear velocity were selected to characterize a correlation between input and output variables. Field and experimental data sets have been collected from different studies. The efficiency of the proposed NF-GMDH-PSO model for both training and testing stages has been investigated. The performance of the NF-GMDH-PSO model were compared with those obtained from the differential evolutionary (DE), model tree (MT), genetic algorithm (GA), artificial neural network (ANN), and traditional empirical equations. Results analysis showed that among the artificial intelligence approach-based equations, DE and GA methods performed better than the other methodologies. The most accurate empirical equations were also introduced. NF-GMDH-PSO network also predicted the longitudinal dispersion coefficient properly and can be considered as an alternative to the aforementioned successful formulas.

Numerical simulation of hydrodynamic and gas mixing processes in underground hydrogen storages

Abstract: The storage of hydrogen in underground reservoirs comprises a potential solution for balancing the fluctuating energy production from wind and solar power plants. In this concept, electrolysers are used to transform excessively produced electrical energy into chemical energy in the form of hydrogen. The resulting large volumes of hydrogen are temporarily stored in subsurface formations purely or in mixture with other gases. In times of high energy demand, the chemical energy is transformed back into electricity by fuel cells or engine generators. Key aspects in the development period and the subsequent cyclic operations of such a storage are the hydrodynamic behavior of hydrogen and its interaction with residual fluids in the reservoir. Mathematically, the behavior can be described by a compositional two-phase flow model with water and gas as phases and all relevant chemical species as components (H2, H2O, CH4, CO2, N2, H2S, etc.). The spatial variation of the gas phase composition between injected and initial gas leads to density and viscosity contrasts which influence the displacement process. The mixing of gases with different compositions is governed by molecular diffusion or mechanical dispersion dependent on the flow velocity. In the present paper, a numerical case study in a depleted gas reservoir was performed. The storage was charged with hydrogen for 5 years. Subsequently, 5 years of seasonal cyclic operation were simulated to predict injection and production rates, pressure response and composition of the produced gas stream .