Browsing by Author "Swati Maurya"

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Cloud computing platforms-based remote sensing big data applications
(Elsevier, 2025) Swati Suman; Swati Maurya; Varsha K. Pandey; Prashant Kumar Srivastava; Dileep Kumar Gupta
Google Earth Engine (GEE) stands as the leading cloud-based geospatial remote sensing data processing platform. GEE repositories contain a range of satellite imageries, which can be used for various environmental applications, thanks to its easy and user-friendly application programming interface (API). One of the most compelling features of GEE includes enabling its users to explore, analyze, and visualize big geospatial data easily, all without requiring access to supercomputers or specialized coding expertise. Remarkably, even a decade after GEE's launch, its impact on remote sensing and geospatial science remains largely unnoticed. In this review, we provide a state-of-the-art report on the usage of cloud computing platforms such as GEE for processing various remote sensing data sources. We further explore the application of GEE for assessing vegetation health, agricultural monitoring, disaster management, image processing, and numerous other environmental applications using GEE. © 2025 Elsevier Ltd. All rights reserved.
Future Climate Change Impact on the Streamflow of Mahi River Basin Under Different General Circulation Model Scenarios
(Springer Science and Business Media B.V., 2023) Swati Maurya; Prashant K. Srivastava; Lu Zhuo; Aradhana Yaduvanshi; R.K. Mall
Climate change (precipitation and temperature) has significantly affected the hydrological regimes and future climate projection. Integration of climate model with physical based model is crucial for quantitative measurement of changes in surface water regime. For accurate estimation, modelling framework need finer scale resolution of climate model output. In this study, we examined the bias corrected, statistically downscale models drawn from the NASA, Earth Exchange Global Daily Downscaled Projections–Coupled Model Intercomparison Project Phase 5 (NEX-GDDP-CMIP5) over the study region. The rainfall and temperature projection output from the INMCM-4, MRI-CGCM3 and their ensemble mean performed well over the Mahi River basin (MRB), India. In this study, the climate data integrated with the SWAT model to analyse the potential impact of climate change on the discharge of MRB. The finding indicates that in the near future (2011–2040) projection of annual average streamflow increases by 76.74% based on the INMCM-4 outputs, 25% based on the MRI-CGCM3 outputs, and 24.53% based on the ensemble mean in comparison to the baseline period (1981–2010). Further, the modelling results of mean monthly streamflow in rainy season indicated that the lowest and highest streamflow changes will be ranging from about 631.07–2718.42 m3/s as observed by INMCM-4, 491.71–2938 m3/s observed by MRI-CGCM3, 513.02–2270.18 m3/s observed by ensemble mean, in the near future. Similarly, in the summer season, the lowest level of stream flow is found to be 158.27 m3/s observed by MRI-CGCM3, 193.38 m3/s (ensemble mean) and 258.53 m3/s (INMCM-4), respectively. Additionally, the streamflow trend was assessed by Mann–Kendall and Sen’s slope method at the monthly, seasonal and annual scales. The future streamflow projection represented the ascending trend observed in south west and winter monsoon, while the descending trend was observed in pre-monsoon and post-monsoon under the INMCM-4, MRI-CGCM3, and ensemble mean. Results on projected precipitation, temperature and streamflow accretion would help to develop effective adaptation measures for reducing the impacts of climate change and to work out long-term water resource management plans in the river basin. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Future projections of crop water and irrigation water requirements using a bias-corrected regional climate model coupled with CROPWAT
(IWA Publishing, 2023) Abhishek Agrawal; Prashant Kumar Srivastava; Vinod Kumar Tripathi; D.J. Shrinivasa; Swati Maurya; Reema Sharma
The study is conducted to examine the climate change impact on rice Crop Water Requirement (CWR) and Net Irrigation Requirement (NIR) using the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) coupled with the CROPWAT 8.0 model. The maximum temperature (Tmax ), minimum temperature (Tmin), and rainfall projections for the baseline (years 1981–2015) and future (years 2030 and 2040) under Representative Concentration Pathway (RCP) 4.5 were derived from NEX-GDDP. To reduce the bias, linear scaling (LS) and the modified difference approach (MDA) were employed. Results show that LS performed better than the MDA along with improved statistical measures such as mean (μ), standard deviation (σ), and percent bias (Pbias), in the case of Tmax and Tmin (μ ¼ 31.14 and 19.63 °C, σ ¼ 5.75 and 6.78 °C, Pbias ¼ 1.43 and 0.33%), followed by rainfall (μ ¼ 2.67 mm, σ ¼ 4.94 mm, and Pbias ¼ 2.4%). The future climatic projections showed an increasing trend in both Tmax and Tmin, which are expected to increase by 1.7 °C by 2040. This would cause an increased range of 1.2 and 2% in 2030 and 2040, respectively. Due to a wide variation in effective rainfall (Peff ), NIR could increase by 4 and 9% in 2030 and 2040, respectively. The above results may help formulate adaptation measures to alleviate the impacts of climate change on rice production. © 2023 The Authors.
Integrated framework for soil and water conservation in Kosi River Basin
(Taylor and Francis Ltd., 2020) Rajani Kumar Pradhan; Prashant K. Srivastava; Swati Maurya; Sudhir Kumar Singh; Dhruvesh P. Patel
Soil loss through erosion and its subsequent deposition is considered as an important challenge for watersheds. In this paper, attempt has been made to integrate the Revised Universal Soil Loss Equation, rainfall climatology from merged IMD gauge-TRMM (1998–2015) and soil hydraulic parameters to delineate the highly susceptible zones of the Kosi River Basin (KRB), Bihar, India for soil erosion assessment and watershed prioritization. The soil hydraulic parameters are calculated by using the ROSETTA model. Afterwards, the analytical hierarchy process based on multi-criteria evaluation method (AHP-MCE) was employed to assign the weighting to each factor (Soil erosion, Compound Factor, Field Capacity) depending on their erosion potential. Weighted overlay analysis is then performed to generate the watershed prioritization map for soil and water conservation. The overall findings suggest that the sub-watersheds 5, 8 and 7 required utmost attention and conservative measures because of their high erodibility characteristics. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
Integrating Soil Hydraulic Parameter and Microwave Precipitation with Morphometric Analysis for Watershed Prioritization
(Springer Netherlands, 2016) Swati Maurya; Prashant K. Srivastava; Manika Gupta; Tanvir Islam; Dawei Han
Morphometric analysis is a promising technique for watershed management. It provides quantitative descriptions of river basin and useful for understanding the behaviour of basin. This study is conducted in Pahuj river basin (Bundelkhand Region) Jhansi, Central India to understand the basin characteristics for watershed prioritization. The Shuttle Radar Topography Mission satellite (SRTM) is used to derive the Digital Elevation Model (DEM) and for creation of thematic layers such as drainage order, drainage density and slope map. In total, 20 mini-watersheds are generated for understanding the morphometric parameters and estimating the compound factor for mini-watersheds. For watershed prioritization, soil hydraulic parameter, compound factor and monthly average monsoon precipitation from TRMM (Tropical Rainfall Measure Mission) for 18 years period (1998–2015) are used. The overall analysis indicates that the mini-watershed numbers 18, 19 needs utmost attention for water conservation followed by mini-watershed number 20. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which morphometric analysis are needed. © 2016, Springer Science+Business Media Dordrecht.
Retrieval of soil moisture deficit through climate change initiative (CCI) soil moisture data and probability distributed model
(Asian Association on Remote Sensing, 2017) Prashant K Srivastava; Swati Maurya; Varsha Pandey; Dharmendra Pandey
Soil moisture deficit (or SMD) is very important variable for many applications such as for flood and drought modelling, which is now possible to estimate using the remote sensing data. This study is an attempt to evaluate the climate change initiative (CCI) soil moisture data for SMD estimation at a catchment scale. The SMD (Soil Moisture Deficit) estimated from the Probability Distribution Model, by using the in situ station data was used as a benchmark for all the comparisons. Approaches based on generalized linear model and relevance vector machine are provided for the estimation of SMD. The overall analysis reveals that CCI soil moisture is of reasonable quality in estimating the soil moisture deficit at a catchment level. Therefore, this study provides first time comprehensive evaluation of CCI soil moisture in Indian condition and the result provides supportive evidence of the potential value of this product for meso-scale studies and hydrological applications. © 2017 Institute of Mechanics and Mechatronics, Faculty of Mechanical, TU Wien. All rights reserved.
Soil erosion in future scenario using CMIP5 models and earth observation datasets
(Elsevier B.V., 2021) Swati Maurya; Prashant K. Srivastava; Aradhana Yaduvanshi; Akash Anand; George P. Petropoulos; Lu Zhuo; R.K. Mall
Rainfall and land use/land cover changes are significant factors that impact the soil erosion processes. Therefore, the present study aims to investigate the impact of rainfall and land use/land cover changes in the current and future scenarios to deduce the soil erosion losses using the state-of-the-art Revised Universal Soil Loss Equation (RUSLE). In this study, we evaluated the long-term changes (period 1981–2040) in the land use/land cover and rainfall through the statistical measures and used subsequently in the soil erosion loss prediction. The future land use/land cover changes are produced using the Cellular Automata Markov Chain model (CA-Markov) simulation using multi-temporal Landsat datasets, while long term rainfall data was obtained from the Coupled Model Intercomparison Project v5 (CMIP5) and Indian Meteorological Department. In total seven CMIP5 model projections viz Ensemble mean, MRI-CGCM3, INMCM4, canESM2, MPI-ESM-LR, GFDL-ESM2M and GFDL-CM3 of rainfall were used. The future projections (2011–2040) of soil erosion losses were then made after calibrating the soil erosion model on the historic datasets. The applicability of the proposed method has been tested over the Mahi River Basin (MRB), a region of key environmental significance in India. The finding showed that the rainfall-runoff erosivity gradually decreases from 475.18 MJ mm/h/y (1981–1990) to 425.72 MJ mm/h/y (1991–2000). A value of 428.53 MJ mm/h/y was obtained in 2001–2010, while a significantly high values 661.47 MJ mm/h/y has been reported for the 2011–2040 in the ensemble model mean output of CMIP5. The combined results of rainfall and land use/land cover changes reveal that the soil erosion loss occurred during 1981–1990 was 55.23 t/ha/y (1981–1990), which is gradually increased to 56.78 t/ha/y in 1991–2000 and 57.35 t/ha/y in 2000–2010. The projected results showed that it would increase to 71.46 t/h/y in 2011–2040. The outcome of this study can be used to provide reasonable assistance in identifying suitable conservation practices in the MRB. © 2020
Techniques and tools for monitoring agriculture drought: A review
(Elsevier, 2024) Varsha Pandey; Prashant K. Srivastava; Anjali Kumari Singh; Swati Suman; Swati Maurya
Drought is a global phenomenon that silently spreads and creates an insidious hazard by destabilizing the hydrological cycle over a large region. Due to increased frequency, severity, and negative impact on climate conditions, droughts have drawn worldwide attention. Real-time drought monitoring and quantification is a prerequisite to ensure the well-being of inhabitants and appropriate management of water, food, and social resources. This chapter provides a comprehensive overview of the agricultural drought and its association with other drought types and monitoring using satellite remote sensing images and various hydrological model-simulated datasets. Furthermore, a few widely used and essential indices for agricultural drought were discussed along with their importance and limitations. Usage of an advanced drought assessment platform and related case studies were also discussed. The chapter concludes with the challenges in agriculture drought monitoring and provides the roadmap for future research. © 2024 Elsevier Inc. All rights reserved.