Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.     

Fluoride contamination of groundwater and its seasonal variability in parts of Purulia district,West Bengal,India

The contamination of aquifers by fluoride and arsenic is a major cause of concern in several parts of India. A study has thus been conducted to evaluate the extent and severity of fluoride contamination and also its seasonal variability. Two blocks (Purulia-1 and Purulia-2) were considered for this purpose. Twenty groundwater samples (in each season) were collected from tube wells during the pre-monsoon and post-monsoon seasons. In addition to fluoride, groundwater samples were also analyzed for major cations, anions, and other trace elements. The concentration of fluoride shows significant seasonal variation and ranges between 0.94–2.52 and 0.25–1.43 mg/l during the pre-monsoon and post-monsoon seasons, respectively. In pre-monsoon season, more than 40% of the water samples show fluoride concentrations higher than the WHO limit. However, during the post-monsoon season, none of the groundwater sample shows fluoride concentrations higher than the WHO limit. Lesser concentration during the post-monsoon season is attributed to the dilution effect by the percolating rainwater, which has also been reflected in the form of a decrease in concentrations of other elements. The petrographic studies of the rock samples collected from the study area show that the rocks are mainly composed of plagioclase, orthoclase, and quartz with abundant biotite. The weathering and dissolution of biotite plays an important role in controlling the fluoride concentrations in the groundwater of the study area.     

The orbit of the Dhajala meteorite

Observations of the trail caused by the meteorite which fell around Dhajala, Gujarat (India), on 28 January 1976 have been used to compute the probable orbit of the meteoroid in space. The cosmic ray effects in the meteorite fragments indicate high mass ablation (?90%), suggesting a high velocity (?20 km/sec) of entry into the Earth's atmosphere. The atmospheric trajectory is reasonably well documented and its deviation from the projected ground fallout can be understood in terms of the ambient wind pattern. The apparent radiant of the trail was at a point in the sky with right ascension 165°, declination +60°. Considering the errors in estimating the radiant, we get a range of orbits with a = 2.3 ± 0.8 AU, e = 0.6 ± 0.1, and i = 28 ± 4° with the constraints of a ? 1.5 AU and V_∞ < 25 km/sec (which causes nearly complete evaporation of the meteoroid). Taking V_∞ = 21.5 lm/sec as indicated by the measured mass ablation of the meteorite, the orbital elements are deduced to be $\text{a = 1.8}\text{AU}\text{, e = 0.59, i = 27°.6, ω = 109°.1, Ω = 307°.8,}\text{and}\text{q = 0.74}$.     

Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal

The goal of this paper is to evaluate and compare the consistency of GIS-based heuristic and bivariate landslide susceptibility mapping techniques in the Himalayan region, taking the Kulekhani watershed of central Nepal as an example. For this purpose, a heuristic and two statistical bivariate landslide susceptibility mapping methods are applied, and three separate landslide susceptibility zonation maps are produced. The maps are compared using three approaches: landslide density analysis, success rate analysis, and agreed area analysis. A comparison of the values obtained from landslide density analysis and the curves of success rate analysis indicate that the two bivariate methods produce almost identical results, whereas the map produced with the heuristic method differs significantly from the others. On the other hand, the agreed area analysis highlights significant spatial differences in the maps obtained from the three methods. Although the three approaches evaluate the consistency of susceptibility maps, only the agreed area analysis is capable of spatially comparing them. Hence, this approach proves to be more suitable for spatially and quantitatively evaluating the consistency of various landslide susceptibility zonation maps.     

Geoelectrical inversion and evaluation of lithology based on optimized Adaptive Neuro Fuzzy Inference System (ANFIS)

Soft computing tools play a vital role in fixing certain non-linear problems related to the earth. More specifically, digging out the mysteries of subsurface of the earth, the nonlinearity can be converging to assemble an approximate solution which resembles the real characteristics of the earth. Adaptive Neuro Fuzzy Inference System (ANFIS) tool is one of the best soft computing tools to estimate the complex data analysis. ANFIS was applied to estimate the subsurface parameters of earth using the Vertical Electrical Sounding (VES) data. Classifying the lithology based on the resistivity values by ANFIS is employed here in this paper. As the resistivity of each formation varies in range of values, ANFIS tool thus approximates the subsurface features based on effective training. In this study, ANFIS performance was checked with training data, and successively it has been tested with the field data. Optimized ANFIS algorithm provides the necessary tool for predicting the non-linear subsurface features. The best training performance of this soft computing tool efficiently predicts the subsurface lithology. Also the interpreted results show the true resistivity and thickness of the subsurface layers of the earth. The proposed technique was represented in Graphical User Interface (GUI), and the lithological variables are predicted in texture format and linguistic variables.     

Rainfall–runoff modelling of railway embankment steep slopes

Abstract

A distributed 1D rainfall–runoff model is presented. It consists of the Saint Venant continuity and momentum equations for overland flow and a modified Green-Ampt model for the infiltration on railway embankment steep slopes. The model is applied to adjacent 10-m-wide erosion control experimental plots with different percentages of grass cover. A relationship between the 2-day antecedent rainfall and initial moisture content was established and used to predict the saturated hydraulic conductivity (Ks). Average values of Ks for 0, 50 and 100% grass cover were found to be 0.1, 1.19 and 2.56 mm/h, respectively. For the majority of cases, the model simulated runoff with acceptable accuracy, 68% having Nash-Sutcliffe efficiency (NSE) values above 0.50. The average NSE value varied between 0.60 and 0.80, with 0% grass-covered plots yielding the highest values. As expected, the runoff volume decreased with increasing percentage of grass cover.

Citation Sajjan, A.K., Gyasi-Agyei, Y., and Sharma, R.H., 2013. Rainfall–runoff modelling of railway embankment steep slopes. Hydrological Sciences Journal, 58 (5), 1162–1176.

Editor D. Koutsoyiannis     

Aquifer system response to intensive pumping in urban areas of the Gangetic plains,India: the case study of Patna

A significant component of domestic demand for water of urban areas located in the Gangetic plains is met by heavy pumping of groundwater. The present study is focused on the Patna municipal area, inhabited by 17 million people and spanning over 134 km², where entire urban water demand is catered from pumping by wells of various capacities and designs. The present study examines the nature of the aquifer system within the urban area, the temporal changes in the water/piezometric level and the recharge mechanism of the deeper aquifers. The aquifer system is made up of medium-to-coarse unconsolidated sand, lying under a ~40-m-thick predominantly argillaceous unit holding 8- to 13-m-thick localised sand layers and continues up to 220 m below ground. Groundwater occurs under semi-confined condition, with transmissivity of aquifers in 5,500–9,200 m² day^?1 range. Hydraulic head of the deeper aquifer remains in 9–19 m range below ground, in contrast to 1–9 m range of that of the upper aquitard zone. The estimated annual groundwater extraction from the deeper aquifer is ~212.0 million m³, which has created a decline of 3.9 m in the piezometric level of the deeper aquifer during the past 30 years. Unregulated construction of deep tube wells with mushrooming of apartment culture may further exacerbate the problem. The sand layers within the aquitard zone are experiencing an annual extraction of 14.5 million m³ and have exhibited stable water level trend for past one and half decades. This unit is recharged from monsoon rainfall, besides contribution from water supply pipe line leakage and seepage from unlined storm water drains.     

Earthquake damage identification: a case study using soft classification approach

The October 8, 2005, Kashmir earthquake (M _w 7.6) affected the rough mountainous regions of India and Pakistan with poor accessibility, and thus, no proper comprehensive ground survey was possible. However, due to the ability of remote sensing technology to acquire spectral measurements of damaged areas at various spatial and temporal scales, extraction of damaged areas can be carried out quickly and with great reliability. The fuzzy-based classifiers [Possibilistic c-Means (PCM), noise cluster (NC), and NC with entropy (NCE)] were applied to identify 2005 Kashmir earthquake, induced landslides, as well as built-up damage (BD) areas, as soft computing approaches using supervised classification. Results indicate that for the identification of landslides and BD areas, NCE classifier generated the best outputs, while for the identification of built-up undamaged areas, NC classifier generated the best output. Further, it was found that the proposed Class Based Sensor Independent (CBSI) technique can improve spectral information of specific class for better identification.     

Arsenic mobilization in alluvial soils of Punjab, North–West India under flood irrigation practices

A laboratory investigation was carried out to examine the mechanism of arsenic (As) mobilization under flooded conditions (24 and 240 h) in 18 alluvial soils of Punjab, North–West India. Total dissolved As increased from a range of 3–16 μg L^?1 (mean 9 μg L^?1) to a range of 33–1,761 μg L^?1 (mean 392 μg L^?1) with the increase in flooding period from 24 to 240 h. The amount of As mobilization varied depending upon redox potential (pe) created by flooding conditions. After 24 h of flooded conditions, pe of soil water suspension ranged from ?1.75 to 0.77 (mean ?0.24). Increasing the flooding period to 240 h, pe of soil water suspension decreased in the range of ?4.49 to ?2.74 (mean ?3.29). Pourbaix diagram identified arsenate (HAsO₄ ^2?) as predominant species in most of the alluvial soil–water suspensions under oxidized conditions, after 24 h of equilibration period, which ultimately transformed to arsenite (H₃AsO₃ ⁰) after 240 h of anaerobic condition due to more reduced status. The solid phase identified was orpiment (As₂S₃). Identification of iron and manganese species in alluvial soil water suspension by Pourbaix diagram indicated decline in both soluble Fe²⁺ and SO₄ ^2? concentration due to the formation of iron sulfide mineral phase after 240 h under anaerobic conditions. In these soils, decline in soluble Fe was also due to the precipitation of vivianite [Fe₃(PO₄)₂·8H₂O]. Elevated arsenic content and low pe value were measured in aquifers located in paddy growing fields comparative to aquifers of other sites. Large degree of variability in As concentrations was recorded in aquifers located at same sites. Thus, it is better to analyze each aquifer for their As content rather than to depends on the prediction on As content of neighbouring wells. The present investigation elucidates that flood irrigation practices in Punjab for growing paddy crop could induce the geochemical conditions favorable to mobilize arsenic from surface soils which could eventually elevate its content in the underlying shallow aquifers. Water abstracted from these aquifers by hand pumps or tube wells for drinking purposes could create hazards for local population due to loading with arsenic concentration above the safe limits. Thus, to avoid further contamination of shallow aquifers with arsenic, it is advisable to shift the flooded rice cultivation to other upland crops having lesser water requirement.