Sananda Kundu

Impact of Climate Change on Future Soil Erosion in Different Slope, Land Use, and Soil-Type Conditions in a Part of the Narmada River Basin, India

AbstractSoil erosion is one of the major hazards affected by the climate change, particularly the changed precipitation trend. The present paper has generated future precipitation by downscaling general circulation model (GCM, HADCM3) data of A2 scenario in a part of the Narmada River Basin in Madhya Pradesh, India, to obtain future impact of climate change on soil erosion. Least-square support vector machine (LS-SVM) and statistical downscaling model (SDSM) models were used for downscaling, and the universal soil loss equation (USLE) model was used for estimating soil loss. The results were analyzed with different slope, land use, and soil category. Outcome showed an increase in future precipitation with the resultant increase in soil erosion, with a positive change of 18.09 and 58.9% in years 2050s and 2080s respectively in LS-SVM, while it is decreasing in the year 2020s (−5.47%). Rate of change of soil erosion with SDSM is 15.52 and 105.80% in years 2050s and 2080s respectively, and decrease in the 20...

Long Term Rainfall Trend Analysis (1871–2011) for Whole India

Climate change has aroused serious consciousness among human beings as it has a strong impact on different parameters like rainfall, temperature, evapotranspiration etc. Change in climatic parameters also affects the agriculture and water demand of an area. The changed pattern of rainfall leads to extreme conditions like flood, drought and cyclones which have increased in frequency in the last few decades making the rainfall trend analysis extremely important for India where a large part of the economy depends upon rain-fed agriculture. The trend of rainfall for 141 years of India was analyzed in the present study from 1871 to 2011. Detection of trend was done by analyzing 306 stations of India divided into seven regions of Homogeneous Indian Monsoon, Core-Monsoon India, North West India, West Central India, Central Northeast India, North East India and Peninsular India. Temporal as well as spatial rainfall variability was shown on monthly, seasonal and annual basis. The Mann–Kendall (MK) Test and Sen’s slope was applied in the study. Mann–Whitney–Pettitt (MWP) test was used to give the break point in the series. Annually, 5 regions have decreasing trend except for core-monsoon and north-east India. Monsoon season depicted decrease in the rainfall magnitude in most of the regions. This result is extremely significant as monsoon rainfall serves the major water demand for agriculture. Change Percentage for 141 years had shown rainfall variability throughout India with the highest increase in North-West India (5.14 %) and decrease in Core-monsoon India (−4.45 %) annually.

Shifting shoreline of Sagar Island Delta, India

Shoreline mapping is extremely important in order to determine the dynamic nature of coastal areas. This paper presents shoreline mapping of the Sagar Island delta, Sundarban region, India. The island is part of mangrove ecosystem and is facing constant erosion and deposition from tidal action and cyclonic storms which have made this an area of unique importance. Mapping of shoreline has been performed 1951 to 2011 and change in the land-water boundary of the island calculated. Further shoreline prediction is performed on the basis of the extracted shorelines using the End point Rate model with a micro-level grid-based approach. The predicted maps have been validated using ground control points. Three images from 1951, 1990 and 2011 have been used for the mapping and detection of changes in the island area and shoreline over 60 years.

Uncertainty analysis of soil erosion modelling using different resolution of open-source DEMs

Abstract The Digital Elevation Model (DEM) is one of the important parameters of soil erosion assessment and notable uncertainties are found in using different resolutions of the DEM. Revised Universal Soil Loss Equation model has been applied to analyze the effect of open-source DEMs with different resolution and accuracy on the uncertainties of soil erosion modelling in a part of the Narmada river basin in Madhya Pradesh in central India. Selected open-source DEMs are GTOPO30 (1 km), SRTM (30 and 90 m), CARTOSAT (30 m) and ASTER (30 m), used for estimating erosion rate. Results with better accuracy are achieved with the high-resolution DEMs (30 m) with higher vertical accuracy than the coarse resolution DEMs with lower accuracy. This study has presented potential uncertainties introduced by the open-source DEMs in soil erosion modelling for better understanding of appropriate selection and acceptable errors for researchers.

Landuse Change Prediction and Its Impact on Surface Run-off Using Fuzzy C-Mean, Markov Chain and Curve Number Methods

The landuse change has considerable impact on the surface run-off of a catchment. With the changing landuse there is reduction in the initial abstraction which results in increasing run-off. This also has effect on future because of constant change in landuse due to urbanization. The Soil Conservation Service Curve Number (SCS-CN) model was used in the study for calculating run-off in a sub-catchment of Narmada River basin for the years 1990, 2000 and 2011 which was further validated with the observed data from the gauges. Stream flow of future for 2020 and 2030 was estimated by this method to observe the impact of landuse change on run-off. The landuse classification was done by Fuzzy C-Mean algorithm. The future landuse prediction for 2020 and 2030 was performed with the Markov Chain Model with 2011 validation. Future run-off was generated on the basis of changing landuse which shows increasing rate of run-off.

Detection of Land Use Change and Future Prediction with Markov Chain Model in a Part of Narmada River Basin, Madhya Pradesh

Landuse and land cover change have significant impact on the environment of a river basin and has gained considerable attention. It has a strong effect on the surroundings where increasing agriculture as well as urban areas has led to the rapid deforestation and changes in the ecology. Present study involves detection of landuse and land cover change in a part of Narmada river of Madhya Pradesh where rapid changes such as irrigation planning is leading to changes in the land cover. Hence, change detection in the present landform and probable changes in the near future is required for planning and management. Landsat images of 1990 (TM), 2000 (ETM+) and 2011 (LISS-III) were used for the classification and future landuse prediction. Supervised Fuzzy C-Mean classification was applied to generate major five classes of water body, built-up area, natural vegetation, agricultural land and fallow land. Overall accuracy for all images was above 85 %. The Markov Chain model was used for prediction. The classified Landsat images of 1990 and 2000 were used to predict the 2011 landuse with Markov Chain which was again validated with the 2011 classified image. The prediction of 2020 and 2030 land use were done to see the future change. The spatial accuracy achieved for the prediction was about 92.5 %. The results illustrate an increase in agricultural land and urban area with the decrease in natural vegetation.

Past, present and future land use changes and their impact on water balance

Landuse change influences the water balance of a region affecting the available water along with the change in the evapotranspiration (ET). The major objectives of this study are to assess the landuse change and its impact on the water balance of the study area, which is a part of the Narmada river basin in Madhya Pradesh, India. Landuse changes of 1990, 2000 and 2011 have been analyzed and the Markov Chain model has been used to predict decadal change of 2020, 2030, 2040 and 2050 landuse. The influence of the past, present and future landuse change on water balance has been analyzed with the SWAT (Soil and Water Analysis Tool) model in the study area. The effect of changes are shown in 12 different sub-watersheds of the area, reflecting an increased water yield (runoff, including ground-water outflow) and surface runoff but decreased ET, which is due to change in the curve number (CN) values (79.85 in 1990 to 84.63 in 2050). Increased CN value in different sub-watersheds of the region has been observed due to a reduction in the vegetation areas, and increase in the agricultural land and settlements. This has caused an increased runoff and decreased ET. The water yield has increased by 6.98% from 1990 to 2011 (1.92 CN increase) and by 17.5% as projected in the 2050 (4.78 CN increase). The actual ET decreases by 3.37% from 1990 to 2011 and by 8.40% in 2050. Simulation with the SWAT using landuse change showed reduction in ET and increased runoff in different sub-watersheds, which needs to be considered in terms of management.

Climate change impact on soil erosion in the Mandakini River Basin, North India

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

Multiple Linear Regression Based Statistical Downscaling of Daily Precipitation in a Canal Command

The climate impact studies, particularly in hydrology, often require climate information at fine scale for present as well as future scenario. Global Climate Model (GCM) estimates climate change scenarios on coarse spatial resolution. Therefore, different techniques have been evolved to downscale the coarse-grid scale GCM data to finer scale surface variables of interest. In the present study, the Statistical Downscaling Model (SDSM) has been applied to downscale daily precipitation from simulated GCM data. SDSM utilizes Multiple Linear Regression (MLR) technique. The daily precipitation data (1961–2001) representing Tawa region has been considered as input (predictand) to the model. The model has been calibrated (1961–1991) and validated (1992–2001) with screened large-scale predictors of (National Centre for Environmental Prediction (NCEP) reanalysis data. The prediction of future daily rainfall for the study area has been carried out for the period 2020s, 2050s and 2080s corresponding to HadCM3 A2 variables. The calibration and validation results confirm the SDSM model acceptability slightly at a lower degree. The results of the downscaled daily precipitation for the future period indicate an increasing trend in the mean daily precipitation.

Crop Identification by Fuzzy C-Mean in Ravi Season Using Multi-Spectral Temporal Images

Information regarding spatial distribution of different crops in a region of multi-cropping system is required for management and planning. In the present study, multi dated LISS-III and AWiFS data were used for crop identification. The cultivable land area extracted from the landuse classification of LISS-III image was used to generate spectral-temporal profile of crops according to their growth stages with Normalised Difference Vegetation Index (NDVI) method. The reflectance from the crops on 9 different dates identified separate spectral behavior. This combined NDVI image was then classified by Fuzzy C-Mean (FCM) method again to get 5 crop types for around 12,000 km2 area on Narmada river basin of Madhya Pradesh. The accuracy assessment of the classification showed overall accuracy of 88 % and overall Kappa of 0.83. The crop identification was done for one entire Ravi season from 23 October 2011 to 10 March 2012.