Prashant K. Srivastava

Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India.

In this work remote sensing, geographic information systems (GIS) and fieldwork techniques are combined in an attempt to identify groundwater potential zones in the hilly terrain of the Pavagarh region. The various thematic maps prepared for delineating groundwater potential zones are lineament density, drainage density, digital elevation model (DEM), slope map and land use/land cover (LULC). A multi-criteria evaluation technique (MCE) is used to investigate a number of choice possibilities and evaluate suitability according to the associated weight of each factor. A map is obtained which shows the classification of the area into good, moderate and low groundwater potential zones.

Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application

AbstractMany hydrologic phenomena and applications such as drought, flood, irrigation management and scheduling needs high resolution satellite soil moisture data at a local/regional scale. Downscaling is a very important process to convert a coarse domain satellite data to a finer spatial resolution. Three artificial intelligence techniques along with the generalized linear model (GLM) are used to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) derived soil moisture, which is currently available at a very coarse scale of ~40 Km. Artificial neural network (ANN), support vector machine, relevance vector machine and generalized linear models are chosen for this study to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) with the SMOS derived soil moisture. Soil moisture deficit (SMD) derived from a hydrological model called PDM (Probability Distribution Model) is used for the downscaling performance evaluation. The statistical evaluation has also been made with the day-time and night-time MODIS LST differences with the mean day and night-time PDM SMD data for the selection of effective MODIS products. The accuracy and robustness of all the downscaling algorithms are discussed in terms of their assumptions and applicability. The statistical performance indices such as R2, %Bias and RMSE indicates that the ANN (R2  = 0.751, %Bias = −0.628 and RMSE = 0.011), RVM (R2  = 0.691, %Bias = 1.009 and RMSE = 0.013), SVM (R2  = 0.698, %Bias = 2.370 and RMSE = 0.013) and GLM (R2  = 0.698, %Bias = 1.009 and RMSE = 0.013) algorithms on the whole are relatively more skillful to downscale the variability of the soil moisture in comparison to the non-downscaled data (R2  = 0.418 and RMSE = 0.017) with the outperformance of ANN algorithm. The other attempts related to growing and non-growing seasons have been used in this study to reveal that season based downscaling is even better than continuous time series with fairly high performance statistics.

Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective

Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting structures by prioritization of mini-watersheds through morphometric analysis. In this study, the morphometric analysis and prioritization of ten mini-watersheds of Malesari watershed, situated in Bhavnagar district of Saurashtra region of Gujarat state, India, are studied. For prioritization of mini-watersheds, morphometric analysis is utilized by using the linear parameters such as bifurcation ratio, drainage density, stream frequency, texture ratio, and length of overland flow and shape parameters such as form factor, shape factor, elongation ratio, compactness constant, and circularity ratio. The different prioritization ranks are assigned after evaluation of the compound factor. Digital elevation model from Shuttle Radar Topography Mission, digitized contour, and other thematic layers like drainage order, drainage density, and geology are created and analyzed over ArcGIS 9.1 platform. Combining all thematic layers with soil and slope map, the best feasibility of positioning check dams in mini-watershed has been proposed, after validating the sites through the field surveys.

Water Harvesting Structure Positioning by Using Geo-Visualization Concept and Prioritization of Mini-Watersheds Through Morphometric Analysis in the Lower Tapi Basin

Geo-visualization concept has been used for positioning water harvesting structures in Varekhadi watershed consisting of 26 mini watersheds, falling in Lower Tapi Basin (LTB), Surat district, Gujarat state. For prioritization of the mini watersheds, morphometric analysis was utilized by using the linear parameters such as bifurcation ratio (Rb), drainage density (Dd), stream frequency (Fu), texture ratio (T), length of overland flow (Lo) and the shape parameter such as form factor (Rf), shape factor (Bs), elongation ratio (Re), compactness constant (Cc) and circularity ratio (Rc). The different prioritization ranks were assigned after evaluation of the compound factor. 3 Dimensional (3D) Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM) and DEM from topo contour were analyzed in ArcScene 9.1 and the fly tool was utilized for the Geo-visualization of Varekhadi mini watersheds as per the priority ranks. Combining this with soil map and slope map, the best feasibility of positioning check dams in mini-watershed no. 1, 5 and 24 has been proposed, after validation of the sites.

Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH Land surface model

Microwave remote sensing and mesoscale weather models have high potential to monitor global hydrological processes. The latest satellite soil moisture dedicated mission SMOS and WRF-NOAH Land Surface Model (WRF-NOAH LSM) provide a flow of coarse resolution soil moisture data, which may be useful data sources for hydrological applications. In this study, four data fusion techniques: Linear Weighted Algorithm (LWA), Multiple Linear Regression (MLR), Kalman Filter (KF) and Artificial Neural Network (ANN) are evaluated for Soil Moisture Deficit (SMD) estimation using the SMOS and WRF-NOAH LSM derived soil moisture. The first method (and most simplest) utilizes a series of simple combinations between SMOS and WRF-NOAH LSM soil moisture products, while the second uses a predictor equation generally formed by dependent variables (Probability Distributed Model based SMD) and independent predictors (SMOS and WRF-NOAH LSM). The third and fourth techniques are based on rigorous calibration and validation and need proper optimisation for the final outputs backboned by strong non-linear statistical analysis. The performances of all the techniques are validated against the probability distributed model based soil moisture deficit as benchmark; estimated using the ground based observed datasets. The observed high Nash Sutcliffe Efficiencies between the fused datasets with Probability Distribution Model clearly demonstrate an improved performance from the individual products. However, the overall analysis indicates a higher capability of ANN and KF for data fusion than the LWA or MLR approach. These techniques serve as one of the first demonstrations that there is hydrological relevant information in the coarse resolution SMOS satellite and WRF-NOAH LSM data, which could be used for hydrological applications.

Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach

Accurate estimation of wind speed is essential for many hydrological applications. One way to generate wind velocity is from the fifth generation PENN/NCAR MM5 mesoscale model. However, there is a problem in using wind speed data in hydrological processes due to large errors obtained from the mesoscale model MM5. The theme of this article has been focused on hybridization of MM5 with four mathematical models (two regression models- the multiple linear regression (MLR) and the nonlinear regression (NLR), and two artificial intelligence models – the artificial neural network (ANN) and the support vector machines (SVMs)) in such a way so that the properly modelled schemes reduce the wind speed errors with the information from other MM5 derived hydro-meteorological parameters. The forward selection method was employed as an input variable selection procedure to examine the model generalization errors. The input variables of this statistical analysis include wind speed, temperature, relative humidity, pressure, solar radiation and rainfall from the MM5. The proposed conjunction structure was calibrated and validated at the Brue catchment, Southwest of England. The study results show that relatively simple models like MLR are useful tools for positively altering the wind speed time series obtaining from the MM5 model. The SVM based hybrid scheme could make a better robust modelling framework capable of capturing the non-linear nature than that of the ANN based scheme. Although the proposed hybrid schemes are applied on error correction modelling in this study, there are further scopes for application in a wide range of areas in conjunction with any higher end models.

Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis

Abstract Appropriate assessment of groundwater is important to ensure sustainable and safe use of this natural resource. However, evaluating overall groundwater quality is difficult due to the spatial variability of multiple contaminants. This research proposes a geographical information system (GIS)-based groundwater quality pollution mapping technique, which synthesizes different available water quality data, normalized with the World Health Organization (WHO) standards. The normalized difference index (NDI) is used to perform the normalization process. This study utilizes a multi-criteria evaluation (MCE) script (MATLAB 10.0), developed to assign weights to each of the analysed water quality parameters. The consistency of judgments of weight assignment is further analysed using the consistency ratio (CR) and consistency index (CI) techniques. The Shuttle Radar Topography Mission (SRTM) C-band radar and Landsat TM satellite image data are used to derive a digital elevation model (DEM) and land-use/land-cover map of the area. A new sensitivity analysis method is introduced to estimate the responsible factors associated with the proposed groundwater pollution zone model (GPZM). Multivariate analysis methods, such as factor analysis (FA), cluster analysis (CA) and principal component analysis (PCA), are used to uncover the latent structure of the data, to understand the correlations across hierarchical levels, and for dimensionality reduction, respectively. Editor D. Koutsoyiannis; Associate editor Chong-yu Xu Citation Srivastava, P. K., Han, D., Gupta, M., and Mukherjee, S., 2012. Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis. Hydrological Sciences Journal, 57 (7), 1453–1472.

Biosorption of As(III) Ion on Rhodococcus sp. WB-12: Biomass Characterization and Kinetic Studies

Biomass obtained from arsenic resistant gram positive bacteria Rhodococcus sp. WB-12 was studied for the removal of arsenite from aqueous solution. The biomass sorption characteristic was investigated as a function of biomass doses, contact time, and pH. The Langmiur Freundlich, and Dubinin-Radushkevich (D-R) models were applied to describe the biosorption isotherm. The biosorption capacity of the biomass for As(III) was found to be 77.3 mg/g (pH 7.0) using 1 g/L biomass with the contact time of 30 min at 30°C. Kinetic evaluation of experimental data showed biosorption of As (III) followed pseudo-second-order kinetics. The Fourier transform infrared spectroscopy (FT-IR) analysis indicated the involvement of possible functional groups (-OH, -C=O, -NH) in the arsenite biosorption process. Thus, biomass derived from Rhodococcus sp. WB-12 cells has potential for use as biosorbent for the removal of arsenic from contaminated water.

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multi-temporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.

Morphometric analysis of Upper Tons basin from Northern Foreland of Peninsular India using CARTOSAT satellite and GIS

The morphometric analysis of river basin helps to explore the interrelationship between hydraulic parameters and geomorphologic characteristics. The study has been conducted in the Upper Tons basin of Northern Foreland of Peninsular India. The river basin has been characterized using the topographical maps, CARTOSAT satellite image integrated using the GIS techniques. The drainage density analysis indicates lower values in the north-eastern regions and thus these regions can be categorized as better ground water potential zone. There are in total 10 sub-watersheds which have been delineated; SW-4 has maximum drainage density (4.75), stream frequency (5.61) and drainage texture (26.64) followed by SW-6–10. The prioritized sub-watershed numbers SW-4 and SW-6–10 need conservation practices because of their high erodibility and run-off. SW-1–3 and SW-5 regions have better permeable bed rocks and hence good for water harvesting. The areal parameter indicates elongated shape of basin and moderate to steeper ground slope. The results are supported by extensive field survey. This study can be applied for soil and water management, as well as disaster prevention from similar type of drainage basins.