Shiv Prasad Aggarwal

Rainfall-runoff and soil erosion modeling using Remote Sensing and GIS technique — a case study of tons watershed

In the present study, the rainfall-runoff relationship is determined using USDA Soil Conservation Service (SCS) method. The coefficient of determination (R2) is 0.99, which indicates a high correlation between rainfall and runoff. The runoff potential map was prepared by assigning individual class weight and scores input map. Annual spatial soil loss estimation was computed using Morgan, Morgan and Finney mathematical model in conjunction with remote sensing and GIS techniques. Higher soil erosion was found to occur in the northern part of the Tons watershed. The soil texture in the affected area is coarse loamy to loamy skeletal and soil detachment is higher. Moreover the land use has open forests, which does not reduce the impact of rainfall. The average soil loss for all the four sub-watersheds was calculated, and it was found that the maximum average soil loss of 24.1 t/ha occurred in the sub-watershed 1.

Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India

Floods adversely affect the life of people and property in the coastal districts. It is important to delineate the flood extent and pattern which helps in the vulnerability assessment and also to find out the intensity of damages to facilitate future planning and management. The study area is a part of the Nuna river basin, which suffers from the flood disasters frequently. The present study applies microwave remote sensing (RADARSAT-1 images) to monitor extent, depth and duration of 2003 and 2008 floods in the Kendrapara district of Odisha, India. RADARSAT-1 images of 4, 11, 13 and 20 September of 2003 and 18, 20, 22 and 24 September of 2008 were used to monitor the flood extent, duration and depth. The threshold method was used to delineate flood extent which was used for calculating flood duration and depth. Further, vulnerability assessment of the paddy crop was done to obtain intensity of damage in the area from the 2003 and 2008 floods. Field survey was done to verify and assess the generated results. Areas affected by more than 15xa0days of flood duration and depth of more than 3xa0m faced maximum loss. Both the years witnessed major floods in this area with an estimated damage of around INR 174 million (

Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India

3.6 million) in 2003 and INR 75 million (

Snow Cover Area Mapping Using Synthetic Aperture Radar in Manali Watershed of Beas River in the Northwest Himalayas

1.6 million) in 2008.

Reservoir Sedimentation Assessment Through Remote Sensing and Hydrological Modelling

Detection and monitoring of seasonal agricultural drought at sub-regional scale is a complex theme due to inefficient spatiotemporal indicators. This study presents a new time-based function of spaceborne soil moisture as an efficient indicator. Bundelkhand of Central India, a frequently agricultural drought affected region, was used as the study area. Rabi agricultural season (October–May) being the dominant agricultural return period, was chosen as the study period. Coarse resolution soil moisture (SMc) obtained from European space agency under climate change initiative program was spatially downscaled (SMd) to meet spatial scale at sub-regional level with overall root-mean-square error under 0.065 cm3/cm3. Indirect validation of SMd was done using temporal impact of rainfall/dry spell on SMd and spatiotemporal impact of SMd on vegetation condition. SMd was found to agree with phenomenon as expected in natural processes and hence it was assumed to be validated. The time-based function derived from spatiotemporal SMd (FSMs) was found to be better related with fluctuations in seasonal crop yield (Ys) at district level as compared to a similar function (FVCIs) derived using vegetation condition index (VCI) from Moderate Resolution Imaging Spectroradiometer. FSMs outperformed FVCIs having better correlation coefficient (R ≥0.8) and Nash–Sutcliffe efficiency coefficient (NSE) than FVCIs for most of the districts. Unlike FVCIs, it also efficiently detected the lowest and highest Ys for majority of the districts representing better association with agricultural drought. Subsequently, frequent soil moisture deficit areas were mapped by using FSMs to visualize the spatiotemporal severity of agricultural drought in the region during Rabi season.

Analyzing future water availability and hydrological extremes in the Krishna basin under changing climatic conditions

The current study has used Synthetic Aperture Radar (SAR) satellite data to estimate the Snow Cover Area (SCA) in Manali watershed of Beas River in Northwest Himalayas of Himachal Pradesh, India. SAR data used in this study is of Radarsat-2 (RS2) and Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR). The SAR preprocessing was done with SAR image processing tools for converting raw SAR images into calibrated geo-coded backscatter images. Maps for forest, built area, layover and shadow were created and used for masking snow cover in these areas. The backscattering ratio of wet snow to reference image threshold method with value range from −2 to −3 db was used to estimate wet SCA for study area. In this technique, if the threshold is too high (≥-2 db) wet SCA is overestimated and if it is too low (≤-3db), this method underestimates the SCA. The wet SCA is under/over estimated (+6xa0% to−8xa0% on average) in late spring season due to the inherent terrain and SAR imaging effects of layover/foreshortening and shadow and also due to the masking of forest areas. Overall, the SCA derived from SAR data matches well when compared with total SCA derived from cloud free optical remote sensing data products, especially during wet season.

Response of hydrological processes to land-cover and climate changes in Kejie watershed, south-west China

AbstractReservoir sedimentation is the gradual accumulation of incoming sediments from upstream catchment leading to the reduction in useful storage capacity of the reservoir.n Quantifying the reservoir sedimentation rate is essential for better water resources management. Conventional techniques such as hydrographic survey have limitations including time-consuming, cumbersome and costly. On the contrary, the availability of high resolution (both spatial and temporal) in public domain overcomes all these constraints. This study assessed Jayakwadi reservoir sedimentation using Landsat 8 OLI satellite data combined with ancillary data. Multi-date remotely sensed data were used to produce the water spread area of the reservoir, which was applied to compute the sedimentation rate. The revised live storage capacity of the reservoir between maximum and minimum levels observed under the period of analysis (2015–2017) was assessed utilizing the trapezoidal formula. The revised live storage capacity is assessed as 1942.258 against the designed capacity of 2170.935xa0Mm3 at full reservoir level. The total loss of reservoir capacity due to the sediment deposition during the period of 41xa0years (1975–2017) was estimated as 228.677xa0Mm3 (10.53%) which provided the average sedimentation rate of 5.58xa0Mm3xa0year1. As this technique also provides the capacity of the reservoir at the different elevation on the date of the satellite pass, the revised elevation–capacity curve was also developed. The sedimentation analysis usually provides the volume of sediment deposited and rate of the deposition. However, the interest of the reservoir authorities and water resources planner’s lies in sub-watershed-wise sediment yield, and the critical sub-watersheds upstream reservoir requires conservation, etc. Therefore, in the present study, Soil and Water Assessment Tool (SWAT) was used for the estimation of sediment yield of the reservoir. The average annual sediment yield obtained from the SWAT model using 36xa0years of data (1979–2014) was 13.144xa0Mm3xa0year−1 with the density of the soil (loamy and clay) of 1.44xa0tonxa0m−3. The findings revealed that the rate of sedimentation obtained from the remote sensing-based methods is in agreement with the results of the hydrographic survey.

Hydrological Modelling Using a Rainfall Simulator over an Experimental Hillslope Plot

The present study aims to investigate the impact of climate change on water availability and hydrological extremes in the Krishna River basin, the second largest eastward draining river of Peninsular India. The hydrological response of the basin for the past observed climatic data (1985–2005) and future climatic scenarios (RCP 4.5 and RCP 8.5, respectively, for 2006–2099) is simulated using the Variable Infiltration Capacity (VIC) model. The soil, vegetation, topographic, and meteorological inputs for the model are derived from remote sensing and field-observed data. The model calibration is performed using observed discharge data at 4 gauging stations for the time period of 21xa0years (1985–2005); the coefficients of determination (R2) are in the range of 0.81–0.95. The model validation is carried out at the Vijayawada station (R2u2009=u20090.82), near the basin outlet. The meteorological forcing consisting of future climatic inputs for the entire century (2006–2099) for RCP 4.5 and RCP 8.5 scenarios extracted from IITMRegCM4-4 predictions are used to simulate the future hydrological regime of the basin. The hydrological response analysis shows increase in annual discharge by 13.8 and 27.8xa0cumec under RCP 4.5 and RCP 8.5, respectively. The hydrological extreme events are also found to increase under RCP 4.5 and RCP 8.5 scenarios as compared with the past records of 1985–2005. The peak discharge is observed to increase in the range of 57.7–76.8 and 68.5–77.5% under RCP 4.5 and RCP 8.5, respectively. The study highlights that the annual water potential and hydrological extremes in the Krishna basin will increase under the future climatic scenarios.