Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged derision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 x 10(6) km(2)), mean annual maximum (12.1 x 10(6) km(2)), and long-term maximum (173 x 10(6) km(2)); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. (C) 2014 Elsevier Inc All rights reserved.

GHG footprint of major cities in India

Concentration of greenhouse gases (GHG) in the atmosphere has been increasing rapidly during the last century due to ever increasing anthropogenic activities resulting in significant increases in the temperature of the Earth causing global warming. Major sources of GHG are forests (due to human induced land cover changes leading to deforestation), power generation (burning of fossil fuels), transportation (burning fossil fuel), agriculture (livestock, farming, rice cultivation and burning of crop residues), water bodies (wetlands), industry and urban activities (building, construction, transport, solid and liquid waste). Aggregation of GHG (CO2 and non-CO2 gases), in terms of Carbon dioxide equivalent (CO(2)e), indicate the GHG footprint. GHG footprint is thus a measure of the impact of human activities on the environment in terms of the amount of greenhouse gases produced. This study focuses on accounting of the amount of three important greenhouses gases namely carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and thereby developing GHG footprint of the major cities in India. National GHG inventories have been used for quantification of sector-wise greenhouse gas emissions. Country specific emission factors are used where all the emission factors are available. Default emission factors from IPCC guidelines are used when there are no country specific emission factors. Emission of each greenhouse gas is estimated by multiplying fuel consumption by the corresponding emission factor. The current study estimates GHG footprint or GHG emissions (in terms of CO2 equivalent) for Indian major cities and explores the linkages with the population and GDP. GHG footprint (Aggregation of Carbon dioxide equivalent emissions of GHG's) of Delhi, Greater Mumbai, Kolkata, Chennai, Greater Bangalore, Hyderabad and Ahmedabad are found to be 38,633.2 Gg, 22,783.08 Gg, 14,812.10 Gg, 22,090.55 Gg, 19,796.5 Gg, 13,734.59 Gg and 91,24.45 Gg CO2 eq., respectively. The major contributors sectors are transportation sector (contributing 32%, 17.4%, 13.3%, 19.5%, 43.5%, 56.86% and 25%), domestic sector (contributing 30.26%, 37.2%, 42.78%, 39%, 21.6%, 17.05% and 27.9%) and industrial sector (contributing 7.9%, 7.9%, 17.66%, 20.25%, 1231%, 11.38% and 22.41%) of the total emissions in Delhi, Greater Mumbai, Kolkata, Chennai, Greater Bangalore, Hyderabad and Ahmedabad, respectively. Chennai emits 4.79 t of CO2 equivalent emissions per capita, the highest among all the cities followed by Kolkata which emits 3.29 t of CO2 equivalent emissions per capita. Also Chennai emits the highest CO2 equivalent emissions per GDP (2.55 t CO2 eq./Lakh Rs.) followed by Greater Bangalore which emits 2.18 t CO2 eq./Lakh Rs. (C) 2015 Elsevier Ltd. All rights reserved.

Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land- cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed. (C) 2015 The Authors. Published by Elsevier B.V.

Reliability Assessment of a Storm Water Drain Network

Most of the cities in India are undergoing rapid development in recent decades, and many rural localities are undergoing transformation to urban hotspots. These developments have associated land use/land cover (LULC) change that effects runoff response from catchments, which is often evident in the form of increase in runoff peaks, volume and velocity in drain network. Often most of the existing storm water drains are in dilapidated stage owing to improper maintenance or inadequate design. The drains are conventionally designed using procedures that are based on some anticipated future conditions. Further, values of parameters/variables associated with design of the network are traditionally considered to be deterministic. However, in reality, the parameters/variables have uncertainty due to natural and/or inherent randomness. There is a need to consider the uncertainties for designing a storm water drain network that can effectively convey the discharge. The present study evaluates performance of an existing storm water drain network in Bangalore, India, through reliability analysis by Advance First Order Second Moment (AFOSM) method. In the reliability analysis, parameters that are considered to be random variables are roughness coefficient, slope and conduit dimensions. Performance of the existing network is evaluated considering three failure modes. The first failure mode occurs when runoff exceeds capacity of the storm water drain network, while the second failure mode occurs when the actual flow velocity in the storm water drain network exceeds the maximum allowable velocity for erosion control, whereas the third failure mode occurs when the minimum flow velocity is less than the minimum allowable velocity for deposition control. In the analysis, runoff generated from subcatchments of the study area and flow velocity in storm water drains are estimated using Storm Water Management Model (SWMM). Results from the study are presented and discussed. The reliability values are low under the three failure modes, indicating a need to redesign several of the conduits to improve their reliability. This study finds use in devising plans for expansion of the Bangalore storm water drain system. (C) 2015 The Authors. Published by Elsevier B.V.

Projected climate change impacts on vegetation distribution over Kashmir Himalayas

Despite high vulnerability, the impact of climate change on Himalayan ecosystem has not been properly investigated, primarily due to the inadequacy of observed data and the complex topography. In this study, we mapped the current vegetation distribution in Kashmir Himalayas from NOAA AVHRR and projected it under A1B SRES, RCP-4.5 and RCP-8.5 climate scenarios using the vegetation dynamics model-IBIS at a spatial resolution of 0.5A degrees. The distribution of vegetation under the changing climate was simulated for the 21st century. Climate change projections from the PRECIS experiment using the HADRM3 model, for the Kashmir region, were validated using the observed climate data from two observatories. Both the observed as well as the projected climate data showed statistically significant trends. IBIS was validated for Kashmir Himalayas by comparing the simulated vegetation distribution with the observed distribution. The baseline simulated scenario of vegetation (1960-1990), showed 87.15 % agreement with the observed vegetation distribution, thereby increasing the credibility of the projected vegetation distribution under the changing climate over the region. According to the model projections, grasslands and tropical deciduous forests in the region would be severely affected while as savannah, shrubland, temperate evergreen broadleaf forest, boreal evergreen forest and mixed forest types would colonize the area currently under the cold desert/rock/ice land cover types. The model predicted that a substantial area of land, presently under the permanent snow and ice cover, would disappear by the end of the century which might severely impact stream flows, agriculture productivity and biodiversity in the region.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Influence of land use changes in river basins on diversity and distribution of amphibians

Land-use changes influence local biodiversity directly, and also cumulatively, contribute to regional and global changes in natural systems and quality of life. Consequent to these, direct impacts on the natural resources that support the health and integrity of living beings are evident in recent times. The Western Ghats being one of the global biodiversity hotspots, is reeling under a tremendous pressure from human induced changes in terms of developmental projects like hydel or thermal power plants, big dams, mining activities, unplanned agricultural practices,monoculture plantations, illegal timber logging, etc. This has led to the once contiguous forest habitats to be fragmented in patches, which in turn has led to the shrinkage of original habitat for the wildlife, change in the hydrological regime of the catchment, decreased inflow in streams,human-animal conflicts, etc. Under such circumstances, a proper management practice is called for requiring suitable biological indicators to show the impact of these changes, set priority regions and in developing models for conservation planning. Amphibians are regarded as one of the best biological indicators due to their sensitivity to even the slightest changes in the environment and hence they could be used as surrogates in conservation and management practices. They are the predominating vertebrates with a high degree of endemism (78%) in Western Ghats. The present study is an attempt to bring in the impacts of various land-uses on anuran distribution in three river basins. Sampling was carried out for amphibians during all seasons of 2003-2006 in basins of Sharavathi, Aghanashini and Bedthi. There are as many as 46 species in the region, one of which is new to science and nearly 59% of them are endemic to the Western Ghats. They belong to nine families, Dicroglossidae being represented by 14 species,followed by Rhacophoridae (9 species) and Ranidae (5 species). Species richness is high in Sharavathi river basin, with 36 species, followed by Bedthi 33 and Aghanashini 27. The impact of land-use changes, was investigated in the upper catchment of Sharavathi river basin. Species diversity indices, relative abundance values, percentage endemics gave clear indication of differences in each sub-catchment. Karl Pearson’s correlation coefficient (r) was calculated between species richness, endemics, environmental descriptors, land-use classes and fragmentation metrics. Principal component analysis was performed to depict the influence of these variables. Results show that sub-catchments with lesser percentage of forest, low canopy cover, higher amount of agricultural area, low rainfall have low species richness, less endemic species and abundant non-endemic species, whereas endemism, species richness and abundance of endemic species are more in the sub-catchments with high tree density, endemic trees, canopy cover, rainfall and lower amount of agriculture fields. This analysis aided in prioritising regions in the Sharavathi river basin for further conservation measures.

Monitoring of seasonal snow cover in Bhutan using remote sensing technique

All major rivers in Bhutan depend on snowmelt for discharge. Therefore, changes in snow cover due to climate change can influence distribution and availability of water. However, information about distribution of seasonal snow cover in Bhutan is not available. The MODIS snow product was used to study snow cover status and trends in Bhutan. Average snow cover area (SCA) of Bhutan estimated for the period 2002 to 2010 was 9030 sq. km, about 25.5% of the total land area. SCA trend of Bhutan for the period 2002-2010 was found to decrease (-3.27 +/- 1.28%). The average SCA for winter was 14,485 sq. km (37.7%), for spring 7411 sq. km (19.3%), for summer 4326 sq. km (11.2%), and for autumn 7788 sq. km (20.2%), mostly distributed in the elevation range 2500-6000 m amsl. Interannual and seasonal SCA trend both showed a decline, although it was not statistically significant for all sub-basins. Pho Chu sub-basin with 19.5% of the total average SCA had the highest average SCA. The rate of increase of SCA for every 100 m elevation was the highest (2.5%) in the Pa Chu sub-basin. The coefficient of variance of 1.27 indicates high variability of SCA in winter.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.