Browsing by Author "Aradhana Yaduvanshi"

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Challenges in geospatial technology for water
(CRC Press, 2016) Prashant K. Srivastava; Aradhana Yaduvanshi
Geospatial technology has revolutionized water resources development and research by expanding our knowledge ofterrestrial environment system and processes. However, geospatial techniques cannot substitute ground-based methods towards providing high quality data at a point scale. e high superiority lies in mapping conditions at regional, continental and even global scales and on a recurring basis. is chapter discusses the importance and value of Remote Sensing and Geographical Information System (GIS) and points out the challenges in this eld that should be taken care of by researchers, policy makers and practitioners in order to view the technology in a proper perspective. INTRODUCTION Earth's physical environment and resources undergo certain, inevitable and virtually constant changes that often seriously affector even threaten the well being of humans. A growing population with limited water resources poses a competing environment for different sectors such as industrial, agricultural and domestic needs for sustenance (Tilman et al. 2002). Water being a physiological necessity and its assessment, conservation and management has great concern for all those who. © 2017 by Taylor & Francis Group.
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.
Integrating TRMM and MODIS satellite with socio-economic vulnerability for monitoring drought risk over a tropical region of India
(Elsevier Ltd, 2015) Aradhana Yaduvanshi; Prashant K. Srivastava; A.C. Pandey
Drought is a recurring feature of the climate, responsible for social and economic losses in India. In the present work, attempts were made to estimate the drought hazard and risk using spatial and temporal datasets of Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) in integration with socio-economic vulnerability. The TRMM rainfall was taken into account for trend analysis and Standardized Precipitation Index (SPI) estimation, with aim to investigate the changes in rainfall and deducing its pattern over the area. The SPI and average rainfall data derived from TRMM were interpolated to obtain the spatial and temporal pattern over the entire South Bihar of India, while the MODIS datasets were used to derive the Normalized Difference Vegetation Index (NDVI) deviation in the area. The Geographical Information System (GIS) is taken into account to integrate the drought vulnerability and hazard, in order to estimate the drought risk over entire South Bihar. The results indicated that approximately 36.90% area is facing high to very high drought risk over north-eastern and western part of South Bihar and need conservation measurements to combat this disaster. © 2015 Published by Elsevier Ltd.
Long-term trend analysis of precipitation and extreme events over kosi river basin in india
(MDPI AG, 2021) Prashant K. Srivastava; Rajani Kumar Pradhan; George P. Petropoulos; Varsha Pandey; Manika Gupta; Aradhana Yaduvanshi; Wan Zurina Wan Jaafar; Rajesh Kumar Mall; Atul Kumar Sahai
Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901-2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann-Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Reference evapotranspiration retrievals from a mesoscale model based weather variables for soil moisture deficit estimation
(MDPI, 2017) Prashant K. Srivastava; Dawei Han; Aradhana Yaduvanshi; George P. Petropoulos; Sudhir Kumar Singh; Rajesh Kumar Mall; Rajendra Prasad
Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon's method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = -0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019-0.667; %Bias range = 2.821-6.894; NSE = 0.013-0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022-0.707; %Bias range = -0.207--6.088; NSE range = 0.013-0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024-0.025; %Bias range = -4.982--3.431; r = 0.245-0.281) than the non-growing season (RMSE range = 0.011-0.12; %Bias range = 33.073-32.701; r = 0.161-0.244) for SMD estimation. © 2017 by the authors.
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
Support vector machines and generalized linear models for quantifying soil dehydrogenase activity in agro-forestry system of mid altitude central Himalaya
(Springer Verlag, 2016) Prashant K. Srivastava; Aradhana Yaduvanshi; Sudhir Kumar Singh; Tanvir Islam; Manika Gupta
In natural ecosystems, the linkages between inputs of carbon from plants, soil moisture (SM) and microbial activity are central to our understanding of nutrient cycling. Predictions of microbial activities in soil are important as they indicate the potential of the soil to support biochemical processes that are essential for the maintenance of soil fertility as well as productivity. The dehydrogenase activity (DHA) in soil provides information on microbial activities of the soil. However, estimation of DHA activity over complex terrain such as soils of the central Himalaya is not always possible due to very harsh environment and climatic conditions. In this study, the attempts were made to estimate the DHA in the soil of mid altitude central Himalaya using computational intelligence techniques. The linear and non-linear correlation results indicate that the fluctuations in SM and organic carbon (OC) in the root zone affect DHA and can be used as predictors for DHA. Therefore, the performances of support vector machines (SVMs) and generalized linear models (GLMs) were attempted for the prediction of DHA over mid altitude central Himalaya using information of SM and OC. The results showed that the SVM was giving a much better performance than GLM using SM and OC and could be promising and cost effective approach for soil DHA prediction over complex ecosystem. 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 SM and OC datasets are used. © 2016, Springer-Verlag Berlin Heidelberg.
Uncertainty in a lumped and a semi-distributed model for discharge prediction in Ghatshila catchment
(MDPI AG, 2018) Aradhana Yaduvanshi; Prashant Srivastava; Abeyou W. Worqlul; Anand Kr Sinha
Hydrologic simulations of different models have direct impact on the accuracy of discharge prediction because of the diverse model structure. This study is an attempt to comprehend the uncertainty in discharge prediction of two models in the Ghatshila catchment, Subarnarekha Basin in India. A lumped Probability Distribution Model (PDM) and semi-distributed Soil and Water Assessment Tool (SWAT) were applied to simulate the discharge from 24 years of records (1982-2005), using gridded ground based meteorological variables. The results indicate a marginal outperformance of SWAT model with 0.69 Nash-Sutcliffe (NSE) for predicting discharge as compared to PDM with 0.62 NSE value. Extreme high flows are clearly depicted in the flow duration curve of SWAT model simulations. PDM model performed well in capturing low flows. However, with respect to input datasets and model complexity, SWAT requires both static and dynamic inputs for the parameterization of the model. This work is the comprehensive evaluation of discharge prediction in an Indian scenario using the selected models; ground based gridded rainfall and meteorological dataset. Uncertainty in the model prediction is established by means of Generalized Likelihood Uncertainty Estimation (GLUE) technique in both of the models. © 2018 by the authors.