Prabhash Kumar Mishra

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.

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.

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.

Climate Change Impact Assessment on Blue and Green Water by Coupling of Representative CMIP5 Climate Models with Physical Based Hydrological Model

Climatic changes have altered hydrological and climatic parameters worldwide, and climate projections suggest that such alterations will continue. In order to maintain the sustainable development and acquire the knowledge of water availability, climatic projection must be coupled with hydrological models. In this study, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models output were integrated with a calibrated hydrological model, Soil and Water Assessment Tools (SWAT) to evaluate the potential effect of climate change on green and blue water over Upper Narmada river Basin (UNB). Therefore, top three representative climate models (MIROC5, CNRM-CM5 and MPI-ESM-LR) from 24 CMIP5 climate models were selected for hydrological modelling. Selected representative climate model outputs were bias corrected by distribution mapping to remove systematic bias correction. Multi-site model calibration approaches indicated Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) as 0.77 and 0.76 for calibration (1978–1995), and 0.73 and 0.70 for validation (1996–2005), respectively. Calibrated model was run for baseline period (1970–2000) and three futuristic period P1 (2011–2040), P2 (2041–2070) and P3 (2071–2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Results indicated annual precipitation decreasing under RCP4.5 and RCP8.5 scenarios changes in green and blue water varying from 16.22 to −14.10% (CNRM,P3) under RCP4.5 and from 38.25 to −22.57% under RCP8.5 with reference to baseline scenario. This study established the sensitivity of UNB to future climatic changes employing projections from CMIP5 climate models and exhibited an approach that applied multiple climate model outputs to estimate potential change over the river basin.

Analysis of Long Term Temperature Trend for Madhya Pradesh, India (1901-2005)

The paper examines the impact of climatic change on the mean temperature time series for Pre-monsoon (Mar-May), Monsoon (Jun-Sept), Post-monsoon (Oct-Nov), winter (Dec-Feb) and Annual (Jan-Dec) at 45 stations in the state of Madhya Pradesh, India. Impact detection is accomplished by using the Mann-Kendall method to find out the monotonic trend and Sen’s slope is method is to identify the grandeur of trend for the period 1901 to 2005 (105 years). Prior to the trend analysis prominence of eloquent lag-1 serial correlation are eradicated from data by the pre-whitening method. In addition, shift year change has also been examined in the study using Pettitt’s test. From 45 stations, most of the station show symbolic hike trend at 5% significance level in the mean temperature time series for Madhya Pradesh region. During peak summer months the maximum temperature touches 40°C in the entire Madhya Pradesh. The magnitudes of annual increase in temperature in the majority of the stations are about 0.01°C.The analysis in the present study indicated that the change point year of the significant upward shift changes was 1963 for annual mean temperature time series, which can be very useful for water resources planners in the study area. The finding of the study provides more insights and inputs for the better understanding of regional temperature and shift behavior in the study area. keywords: Monotonic trend analysis, Sen’s slope, Mann-Kendall test, Pettit’s test, serial correlation, Temperature.