ORCID Profile
0000-0002-3963-265X
Current Organisation
University of Technology Sydney
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Publisher: Springer Science and Business Media LLC
Date: 04-2023
Publisher: Springer Science and Business Media LLC
Date: 13-12-2020
Publisher: MDPI AG
Date: 26-08-2021
DOI: 10.3390/W13172344
Abstract: Increased population and increasing demands for food in the Indo-Gangetic plain are likely to exert pressure on fresh water due to rise in demand for drinking and irrigation water. The study focuses on Bhojpur district, Bihar located in the central Ganga basin, to assess the groundwater quality for drinking and irrigation purpose and discuss the issues and challenges. Groundwater is mostly utilized in the study area for drinking and irrigation purposes (major crops sown in the area are rice and wheat). There were around 45 groundwater s les collected across the study region in the pre-monsoon season (year 2019). The chemical analytical results show that Ca2+, Mg2+ and HCO3− ions are present in abundance in groundwater and governing the groundwater chemistry. Further analysis shows that 66%, 69% and 84% of the s les exceeded the acceptable limit of arsenic (As), Fe and Mn respectively and other trace metals (Cu, Zn, Pb, Cd) are within the permissible limit of drinking water as prescribed by Bureau of Indian Standard for drinking water. Generally, high As concentration has been found in the aquifer (depth ranges from 20 to 40 m below ground surface) located in proximity of river Ganga. For assessing the irrigation water quality, sodium adsorption ratio (SAR) values, residual sodium carbonate (RSC), Na%, permeability index (PI) and calcium alteration index (CAI) were calculated and found that almost all the s les are found to be in good to excellent category for irrigation purposes. The groundwater facie has been classified into Ca-Mg-HCO3 type.
Publisher: Springer Science and Business Media LLC
Date: 30-07-2018
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 12-2019
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-5398
Abstract: & & Aspect-controlled vegetation over opposing hillslopes are driven by non-uniform distribution of incoming solar radiation in semi-arid ecosystems. This leads to variation in soil and vegetation characteristics. In mid- to high-latitudes where available soil moisture is a limiting factor for vegetation growth, poleward-facing slopes develop denser vegetation cover providing greater erosion protection than the equatorward-facing hillslopes. The variation in erosion rates across opposing hillslopes leads to the development of topographic asymmetry of hillslopes over long timescales. This asymmetry is quantified by the hillslope asymmetry index (HAI), a metric given as the ratio of the median slope angles of opposite hillslopes. We present a combined approach of modelling and observed data analysis to investigate the relationships of HAI with climatological, geomorphic, and ecologic variables at a global scale. We analysed these relationships using digital elevation topographic data to compute observed HAI for 80 different catchments across the world, where aspect-controlled vegetation has been reported in the literature. Further, we used the CHILD landscape evolution model (LEM), which uses the continuity equation for water, sediment, and biomass, to investigate the control of climatological, geomorphic, and ecologic variables on the development of hillslope asymmetry through a modelling approach,. The outcomes of the study highlights that latitude and mean topographic gradient are the two dominant factors affecting hillslope asymmetry due to their vital role in controlling vegetation density through the modulation of incoming solar radiation. These results improve our understanding on how different climatic variables and geographic properties affect the magnitude of hillslope asymmetry and their implications on landform evolution modelling.& &
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 12-2019
Publisher: IWA Publishing
Date: 03-2023
DOI: 10.2166/WCC.2023.512
Abstract: The present study addresses the possible effects of soil moisture changes on the simulated daily maximum and minimum air temperatures of Australia for a duration of 13 years. Therefore, the community land model version 4.5 (CLM4.5 coupled to the RegCM4) was used to represent the soil moisture and processes associated with it. The CLM4.5 has two land-surface hydrology schemes: TOPMODEL (TOP) and Variable Infiltration Capacity (VIC) and two simulations were conducted, namely: TOP and VIC. The results showed that VIC has lower soil moisture than TOP, leading to a decrease in vegetation transpiration, evaporation, and an increase in soil evaporation relative to TOP. However, there is no considerable difference between the two simulations compared with reanalysis products. In comparison to in-situ measurements, the RegCM4 can reasonably model the climatological annual cycle of mean air temperature (TMP) and its performance varies with the study site (e.g., RegCM4 overestimates TMP by 2.76 and 5.46 °C at Yanco and Tumbarumba, respectively). In summary, the simulated maximum and minimum air temperatures are sensitive to the physical parameterization of RegCM4 rather than variations in soil moisture. Likewise, improvements to the land-surface hydrology schemes TOP/VIC are required to better model Australia's daily maximum and minimum air temperatures.
Publisher: MDPI AG
Date: 02-02-2023
DOI: 10.3390/SU15032679
Abstract: The present investigation evaluated the effect of continuous application ( years) of organic and inorganic fertilisers on soil aggregate stability, aggregate size distribution, aggregate-associated carbon and its fractions, and total macro-nutrient content under the soybean–wheat cropping system in vertisols of the semi-arid region. Seven contrasting treatments consisted of T1 (50% NPK), T2 (100% NPK), T3 (150% NPK), T4 (100% NP), T5 (100% N), T6 (100% NPK + FYM) and T7 Control (crop raised without addition of any nutrient). The highest and lowest percentage of large macroaggregates (11.3%) was found in T6 and T7 treatments. The NPK + FYM (T6) treatments substantially increased the proportion of the macroaggregate fractions ( mm and 2–0.25 mm) than other treatments. However, different manure and fertilisation treatments did not affect the proportion of silt + clay aggregates. Long-term application of 100% NPK + FYM increased mean weight diameter (MWD) and stable water aggregates (WSA) by 35.7 and 6.01% over control. The aggregate-associated SOC followed the trend of large macroaggregates microaggregates small macroaggregates silt + clay fractions. Application of long-term manure plus inorganic fertiliser (T6) has also increased Walkley Black soil organic carbon (WBSC), permanganate oxidisable carbon (KMnO4-C), soil microbial biomass carbon (SMBC), carbon mineralisation (CM), total soil carbon (TSC), total soil N (TSN), total soil phosphorus (TSP) and total soil potassium (STK) by 82.1, 71.6, 182, 42.4, 23.9, 41.6, 117 and 18.4%, respectively, over control (T7). The lowest metabolic quotient (MetQ) value of 5.13 mg CO2–C mg−1 MBC h−1 was obtained in the control treatment (T7). The lowest MetQ was recorded in the integrated application of manure + inorganic fertiliser, i.e., 100% NPK + FYM (T6). Similarly, microbial quotient (MiQ) was also higher in treatment T6 (100% NPK + FYM) and lower in T7 (control). It is concluded that the application of inorganic fertiliser alone is insufficient to maintain soil health and sustainability so, combined application of manure plus inorganic fertilisation is the most important nutrient management practice for long-term soil sustainability because it maintains SOC levels in soils for long periods and ultimately ensures the soil health of soybean–wheat cropping systems in the vertisols of semi-arid regions.
Publisher: Springer Science and Business Media LLC
Date: 23-09-2020
Publisher: Springer Science and Business Media LLC
Date: 21-07-2021
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier
Date: 2022
Publisher: MDPI AG
Date: 17-05-2023
DOI: 10.3390/LAND12051083
Abstract: Water erosion is one of the major land degradation problems all over the globe, and its accurate quantification in different land use contexts is required in order to propose suitable conservation measures and curtail related hazards. In the Andaman and Nicobar (A& N) Islands, the land use changes due to faster urbanization and deforestation practices have led to accelerated erosion at many points around the inhabited Islands. Moreover, agricultural land uses in the A& N Islands are vulnerable to severe soil erosion, mainly due to cultivation practices along the steep slopes and mono-cropping culture. A study was conducted by establishing runoff plots in areas with different land uses to measure soil and nutrient losses and to estimate soil erosion using a semi-process-based soil erosion model, i.e., Revised Morgan Morgan and Finney (RMMF). The RMMF model was calibrated using primary data from runoff plots for the years 2019–21, validated for the year 2022, and applied in a Geographical Information System (GIS) to estimate soil erosion spatially over the Andaman ecosystem. The RMMF model simulated soil erosion during validation with a coefficient determination (R2) greater than 0.87 as compared to measured soil erosion from the runoff plots. The study revealed that annual N, P, and K losses of 41–81%, 42–95%, and 7–23%, respectively, due to runoff from various land uses. The land use land classification analysis of the Andaman Islands revealed that about 88% of the total geographical area is under the forest and mangrove land uses, which exhibited very slight soil erosion of t/ha. This 88% of forest and mangrove areas requires suitable conservation measures such as afforestation and rehabilitation/restoration of mangroves. Moreover, 6% of cultivated areas need terracing, bunding, intercropping, etc., at the highest priority in order to conserve a sustainable Andaman ecosystem. On average, the annual soil loss from the Andaman Islands is 3.13 t/ha. About 6% of the study area exceeds the soil tolerance limit of 2.5–12.5 t/ha/year, which needs suitable soil and water conservation measures at the lowest priority due to economic implications.
Publisher: Springer Science and Business Media LLC
Date: 17-04-2021
Publisher: Springer Science and Business Media LLC
Date: 18-08-2020
Publisher: Wiley
Date: 10-07-2022
DOI: 10.1002/ESP.5427
Abstract: Topography affects the intensity and spatial distribution of precipitation due to orographic lifting mechanisms and, in turn, influences the prevailing climate and vegetation distribution. Previous modelling studies on the impact of orographic precipitation on landform evolution have considered bare soil conditions. However, research on the effect of changes in precipitation regimes induced by elevation gradients (particularly in aspect‐controlled semi‐arid ecosystems) on landform patterns, trying to understand feedbacks and consequences for coevolving vegetation, has been limited. In this study, the Channel–Hillslope Integrated Landscape Development (CHILD) landscape evolution model coupled with the vegetation dynamics Bucket Grassland Model (BGM) is used to analyse the coevolution of semi‐arid landform–vegetation ecosystems. The CHILD+BGM model is run under different combinations of precipitation and solar radiation settings. Three precipitation settings, including uniform, elevation control, and orographic control on precipitation, are considered in combination with spatially uniform and spatially varied radiation settings. Based on the results, elevation control, aspect, and drainage network are identified as the major drivers of the distribution of vegetation cover on the landscapes. Further, the combination of orographic precipitation and spatially varied solar radiation created the highest asymmetry in the landscape and ide migration due to the emergence of gentler slopes on the windward than the leeward sides of the domain. The modelling outcomes from this study indicate that aspect control of solar radiation in combination with orographic precipitation plays a key role in the generation of topographic asymmetry in semi‐arid ecosystems.
Publisher: Wiley
Date: 03-07-2020
Publisher: Wiley
Date: 07-12-2020
DOI: 10.1002/HYP.13990
Publisher: Springer Science and Business Media LLC
Date: 30-11-2021
Publisher: MDPI AG
Date: 29-04-2021
DOI: 10.3390/RS13091716
Abstract: Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.
Publisher: Springer Science and Business Media LLC
Date: 27-11-2019
Publisher: MDPI AG
Date: 03-09-2021
DOI: 10.3390/S21175934
Abstract: Crop geometry plays a vital role in ensuring proper plant growth and yield. Check row planting allows adequate space for weeding in both direction and allowing sunlight down to the bottom of the crop. Therefore, a light detection and ranging (LiDAR) navigated electronic seed metering system for check row planting of maize seeds was developed. The system is comprised of a LiDAR-based distance measurement unit, electronic seed metering mechanism and a wireless communication system. The electronic seed metering mechanism was evaluated in the laboratory for five different cell sizes (8.80, 9.73, 10.82, 11.90 and 12.83 mm) and linear cell speed (89.15, 99.46, 111.44, 123.41 and 133.72 mm·s−1). The research shows the optimised values for the cell size and linear speed of cell were found to be 11.90 mm and 99.46 mm·s−1 respectively. A light dependent resistor (LDR) and light emitting diode (LED)-based seed flow sensing system was developed to measure the lag time of seed flow from seed metering box to bottom of seed tube. The average lag time of seed fall was observed as 251.2 ± 5.39 ms at an optimised linear speed of cell of 99.46 mm·s−1 and forward speed of 2 km·h−1. This lag time was minimized by advancing the seed drop on the basis of forward speed of tractor, lag time and targeted position. A check row quality index (ICRQ) was developed to evaluate check row planter. While evaluating the developed system at different forward speeds (i.e., 2, 3 and 5 km·h−1), higher standard deviation (14.14%) of check row quality index was observed at forward speed of 5 km·h−1.
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2023
DOI: 10.1145/3584317
Abstract: This introduction welcomes all readers to this ACM JETC special issue on CAD for Security: Pre-silicon Security Sign-off Solutions Through Design Cycle. The articles published in this special issue reflect how computer-aided design (CAD) tools are developed to expand the notion of automated security verification throughout the system-on-chip (SoC) design cycle. This special issue aims to demonstrate how the semiconductor industry must look for security-oriented metrics and evaluation as part of automatic CAD solution development to aid analysis, identifying, root-causing, and mitigating SoC security problems. Throughout this introductory note, we first represent the need for such a security-oriented sign-off solution for the ASIC design flow, then it is followed by providing an overview of the articles published in this special issue and how they address such requirements.
Publisher: MDPI AG
Date: 22-02-2023
DOI: 10.3390/W15050856
Abstract: Mismanagement of fresh water is a primary concern that negatively impacts agricultural productivity. Judicious use of water in agriculture is possible by estimating the optimal requirement. The present practice of estimating crop water requirements is using reference evapotranspiration (ET0) values, which is considered a standard method. Hence, predicting ET0 is vital in allocating and managing available resources. In this study, different machine learning (ML) algorithms, namely random forests (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB), were optimized using the naturally inspired grey wolf optimizer (GWO) viz. GWORF, GWOXGB, and GWOLGB. The daily meteorological data of 10 locations falling under humid and sub-humid regions of India for different cross-validation stages were employed, using eighteen input scenarios. Besides, different empirical models were also compared with the ML models. The hybrid ML models were found superior in accurately predicting at all the stations than the conventional and empirical models. The reduction in the root mean square error (RMSE) from 0.919 to 0.812 mm/day in the humid region and 1.253 mm/day to 1.154 mm/day in the sub-humid region was seen in the least accurate model using the hyperparameter tuning. The RF models have improved their accuracies substantially using the GWO optimizer than LGB and XGB models.
Publisher: MDPI AG
Date: 30-06-2021
DOI: 10.3390/CLI9070109
Abstract: The Himalayas constitute one of the richest and most erse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.
Publisher: MDPI AG
Date: 10-08-2022
DOI: 10.3390/CLI10080116
Abstract: There was an error in the original publication [...]
Publisher: MDPI AG
Date: 31-01-2023
DOI: 10.3390/RS15030796
Abstract: Anthropically-induced land-use/land cover (LULC) changes create an imbalance between water and energy fluxes by affecting rainfall-runoff partitioning. This alters the catchment’s flow regime, generating increased highs and reduced low flows, triggering socio-economic and environmental damages. The focus of this study is two-fold (i) to quantify the hydrological changes induced in the urbanizing watershed and (ii) to analyze changes in streamflow variability and generation of extremes (high- and low-flow), using the soil and water assessment tool (SWAT) for Peachtree Creek, USA. The results indicate that the change in LULC significantly influences the availability of soil moisture, ET, and contribution to groundwater flow. It is observed that the variations in these processes regulate the water availability from the surface and sub-surface sources, thus affecting the generation of extreme flows. The spatio-temporal analysis, in response to LULC changes, indicates that (i) urbanization significantly affects baseflow, and its variability depends on the degree of urbanization and the predominant land-use class of the subwatersheds, and (ii) the seasonal variations in the baseflow contribution to the streams depend on ET and the timing and magnitude of groundwater outflow to streams. These variations in ET and groundwater lead to water excess/deficit regions, thus increasing the susceptibility to floods during heavy precipitation events and reducing the reliability of streams during dry periods. Thus, in an urbanizing watershed, the hydrological regime of the watershed may not always be a function of changes in the surface runoff, but will be modified by ET and groundwater dynamics. Further, the study shows that the changes in model parameters can provide insight into the implications of LULC changes on hydrological processes and flow regimes. Evaluating the implications on the basin water balance is paramount for deriving any management operations and restoration activities. The study also outlines the significance of analyzing the spatial and temporal scale streamflow variations for managing water resources to reduce damage to lives and properties.
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2017
Publisher: American Geophysical Union (AGU)
Date: 24-07-2020
DOI: 10.1029/2020GL088918
Publisher: MDPI AG
Date: 20-02-2023
DOI: 10.3390/RS15041143
Abstract: Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the s le plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.
No related grants have been discovered for Ankur Srivastava.