Browsing by Author "Shwetha, H.R."

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    A Review: Contribution of HEC-HMS Model
    (Springer Science and Business Media Deutschland GmbH, 2023) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.
    The rapid increase of population worldwide, urbanization, and industrialization significantly impact hydrologic processes locally and globally. Thus, development planning and managing various water resources are required to meet multiple water demands. However, acquiring gauge discharge data has always been difficult since measurements cannot be taken at every point along the river. Thus, HEC-HMS (Hydrologic Modeling System) is the hydrological model that can transform rainfall into a runoff by using known parameters, data, and appropriate mathematical equations to simulate flow records at the desired location. HEC-HMS was developed by the USACE and is freely accessible. It can estimate runoff from rainfall. In this paper, we review the studies carried out by researchers on the HEC-HMS model worldwide to ascertain its ability to simulate runoff with accuracy and use for making decisions. It could be seen that many researchers compared different modelling methods to obtain the best model suitable under different hydrological conditions and found HEC-HMS as a good model over others and recommended it for simulation of runoff. The reviews show that the HEC-HMS rainfall-runoff model has many flood modelling and water resource planning and management applications. In most studies, HEC-HMS rainfall-runoff modelling was found to be efficient and dependable in predicting runoff accuracy in various river basins. As a result, the model can simulate runoff in an ungauged basin for water resource planning, development, management, and decision-making. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Comparative Analysis of Topographic Factor (LS Factor) Estimation Methods for Soil Erosion Risk Assessment in the Netravati Watershed, India
    (Springer Science and Business Media Deutschland GmbH, 2024) Makhdumi, W.; Shwetha, H.R.; Dwarakish, G.S.
    Soil erosion models are crucial for soil conservation planning and environmental assessments globally. The Universal Soil Loss Equation (USLE)-based models are most popular empirical models for estimating soil erosion. The topographic factor (LS factor), which combines slope length and slope steepness, significantly affects soil loss among the input parameters. This study aimed to estimate the LS factor for the Netravati watershed in Karnataka, India, which has a high elevation variation (0–1719 m) and diverse terrain, posing soil erosion and local landslide risks that require conservation planning. Three different methods were employed for dimensionless LS factor estimation: Wischmeier and Smith equation (Method I), Moore and Burch equation (Method II), and Desmet and Govers equation (Method III) along with Geographic Information System (GIS) techniques and a high-resolution (12.5 m) ALOS-PALSAR Digital Elevation Model (DEM). The LS factor values ranged from 0.06 to 1549.39 for Method I, 0 to 191.72 for Method II, and 0.03 to 149.09 for Method III. The results showed that Method III produced values that were evenly distributed throughout the spatial domain, with a mean value of 3.31 and a standard deviation of 4.87. Method I generated very high LS factor values along the flow path and almost uniform values for the rest of the study area. The mean LS factor values for Method I and Method II were 8.31 and 7.41, respectively, with standard deviations of 18.74 and 9.88. The findings of this research suggest that Method III is preferable approach for estimating LS factor values in a spatial domain due to its even distribution of values and low standard deviation. This study demonstrates that estimating the LS factor is impacted by the availability and accuracy of topographic data and the technique used. The findings can be used to support sustainable land management practices in the study area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Crop Classification Based on Optimal Hyperspectral Narrow Bands Using Machine Learning and Hyperion Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Reddy, B.S.; Sharma, S.; Shwetha, H.R.
    In view of global climate change and the limited availability of cropland, crop classification plays a critical role in maintaining food security. Hyperspectral remote sensing has emerged as a valuable tool for classifying crops using detailed spectral information. To explore the potential of hyperspectral data for nationwide crop classification, the research uses the GHISACONUS library to identify Optimal Hyperspectral Narrow Bands (OHNBs) across seven Agricultural Experimental Zones (AEZ) in the USA. Principal Component Analysis (PCA) techniques are employed to identify 24 OHNBs from the data. OHNBs achieved notable accuracy rates, ranging from 75% to 91% when classifying different crop types and their growth stages. However, accuracy drops below 90% in significant cases, likely due to the limited selection of 24 OHNBs and the variation in crop phenology across the seven study areas. The research indicates that systematically selecting OHNBs based on crop phenological stages consistently achieves satisfactory classification accuracy. This approach effectively classifies crops in any Hyperion image. Overall, the study contributes significantly to our knowledge of using OHNBs for nationwide crop classification, highlighting the importance of considering phenological stages and data acquisition conditions to enhance accuracy. © 2023 IEEE.
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    ENHANCING SPATIAL RESOLUTION OF GPM RAINFALL DATA IN UPPER CAUVERY BASIN, INDIA: MACHINE LEARNING APPROACH
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, P.G.; Saicharan, V.; Shwetha, H.R.
    Spatial downscaling is an effective way to obtain rainfall with sufficient spatial details. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite rainfall products is 0.1° × 0.1°, which is too coarse for regional-scale analysis. This study employed two Model averaging methods (Random Forest + XGBoost, Random Forest + CatBoost), Ensemble methods (Random Forest, XGBoost, CatBoost) and Stacked Random Forest + XGBoost model for downscaling GPM IMERG monthly rainfall over the Upper Cauvery Basin from 2015 to 2020 from 0.1°(~ 10 km) to 1 km resolution. Five land surface variables (auxiliary variables), NDVI, elevation, LST, slope, and aspect, were employed for this purpose. The stacked RFR+XGB model outperformed the model averaging techniques, achieving a higher R2 of 0.694 and a lower RMSE/MAE of 44.57/35.23. While the ensemble method yielded promising results, it struggled to predict extreme rainfall values. The downscaled dataset facilitates improved hydrological applications, including water footprint analysis, hydrological monitoring, and disaster warning systems. © 2024 IEEE.
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    Erosion and Accretion in the Netravati River Stretch: Spatiotemporal Analysis Using Geospatial Approach
    (Springer Science and Business Media Deutschland GmbH, 2024) Makhdumi, W.; Shwetha, H.R.; Dwarakish, G.S.
    Understanding erosion and accretion, which are critical geomorphic processes, is essential for effective river management and conservation. Erosion by removing soil and rock changes the river's shape, depth, and course. Accretion, conversely, involves the deposition and accumulation of sediment, shaping features like riverbanks and floodplains. Focused on a 30 km stretch of the Netravati River, in the southwestern region of India, this study used Survey of India toposheets and Landsat images to track changes over time (1973, 1998, 2022). The Normalised Difference Water Index (NDWI) and image classification were employed for the analysis which revealed notable spatiotemporal variations in these processes. From 1973 to 2022, the analysis estimated a total erosion of 510.43 hectares and an accretion of 317.71 hectares. The years 1973–1998 witnessed more accretion (417.6 hectares) than erosion (229.08 hectares). And, from 1998 to 2022, erosion dominated at 438.37 hectares, with only 56.97 hectares of accretion. These variations can be attributed to both natural processes and human interventions. Notably, the construction of a vented dam in 1993 at Thumbe, followed by the subsequent dam in 2016, 50 m downstream of the old dam, influenced the sediment dynamics and flow patterns in the Netravati River, potentially impacting erosion and accretion processes. This research adds to our understanding of erosion and sediment changes in the Netravati River over time. The dams and hydraulic structure upstream along with geospatial techniques offer researchers and river managers a unique opportunity to examine river shape impacts and thus develop sustainable strategies for river preservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Estimation of daily actual evapotranspiration using vegetation coefficient method for clear and cloudy sky conditions
    (Institute of Electrical and Electronics Engineers, 2020) Shwetha, H.R.; Nagesh Kumar, D.N.
    Actual evapotranspiration (AET) can be studied and estimated using remote-sensing-based methods at multiple spatial and temporal scales. Reflectance and Land surface temperature are essential in these methods. However optical and thermal sensors fail to provide these data under overcast conditions and this creates gap in the AET product. Besides, there is a necessity of the AET method that requires less data and estimates AET with better accuracy. In this regard, AET was estimated for all-sky conditions using the vegetation coefficient (VI-Kv) method utilizing microwave, thermal, and optical data. Essential reference evapotranspiration (ET0) under cloudy conditions was estimated using LST-based Penman-Monteith temperature (PMT) and Hargreaves-Samani equations. Furthermore, LST predicted using the microwave polarization difference index (PLST) and LST of moderate resolution imaging spectroradiometer (MODIS) cloud product (MLST) were evaluated with in-situ air temperature (Ta) under cloudy sky conditions. Results revealed that the PLST correlated better with Ta than MLST with correlation coefficient (r) values of 0.71 and 0.81 for day and night times, respectively. Hence, PLST-based solar radiation (Rs) estimation yielded better accuracy with observed Rs with r and root mean square error values of 0.864 and 0.07 for Berambadi station under cloudy conditions, respectively. PMT-based ET0 values corresponded well with the observed ET0 under cloudy sky condition during this study. In addition, AET estimated using the VI-Kv method was compared with the simple two-source energy balance (TSEB) method under clear sky conditions. It was found that the improved VI-Kv method performed better than the TSEB method and could also fairly estimate AET even under cloudy sky conditions. © 2008-2012 IEEE.
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    Flood Inundation Mapping of Krishnaraja Nagar, Mysore Using Sentinel-1 Sar Images
    (Springer Science and Business Media Deutschland GmbH, 2024) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.
    Floods cause physical damage and impact the availability of food, water, and crops. Effective disaster management and disaster risk reduction strategies require a quick and accurate mapping of these phenomena. The study area selected is the Krishnaraja Nagar taluk, Mysore districts, Karnataka having an area of 608 Km2. In this study, the analysis of a flood event was conducted using the temporal GRDH SAR pictures in C-band from Sentinel-1. Additionally, the co-polarized Vertical transmit, and Vertical received (VV) Synthetic Aperture Radar (SAR) images were utilized to map the extent of the flooded area. Two methods of change detection are applied to the temporal SAR images: Otsu's Automatic thresholding method using Matlab R2020a, utilizing a pre-flood image dated 02 August 2018 that shares identical image characteristics with the flood images captured on 14 August 2018; and flood mapping based on Normalized Difference Flood Index (NDFI) using Sentinel Application Platform (SNAP) software. By dividing the SAR image's non-water and open-water regions, the threshold approach was used to extract the flooded areas. In order to identify the actual flooded region, permanent water bodies were later removed from the open water. An analysis of the overlay flood maps was conducted to determine the total area inundated. After processing the SAR data and conducting threshold operations, the flooded area estimates from NDFI is 28.10 km2, and by Otsu's method flooded area is 21.92 km2. It is concluded from the study that the SAR information, sideways with GIS, can be used efficiently for floodwater plotting, real-time analysis, and analysing the spread of floodwater in a flood-prone zone. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Flood Modelling and Mapping of Harangi River, Tributary of Cauvery River
    (Springer Science and Business Media Deutschland GmbH, 2024) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.
    Identifying and mapping the flood-prone area is a vital element of any flood management programme. Hydraulic modelling and remote sensing have been used for decades to predict flood events. In this study, unsteady flow analyses have been performed using the Hydrologic Engineering Centre-River Analysis System (HEC-RAS) software. The geometry file is created using the RAS Mapper tool. The study area selected is a 68 km stretch of the Harangi River from Kudige (12° 31′N, 75° 57′E) to Chunchunkatte (12° 30′24′′N, 76° 18′0′′E) gauging station in Karnataka. The required discharge data is collected from Central Water Commission, Bangalore. Manning’s roughness coefficient (n) is used as a simulating parameter to perform inundation mapping for the years 2018 and 2019, as the discharge in the river is high (2435, 2297 m3/s). Gumbel, Log-Pearson Type-3 (LP3) and Log-Normal (LN) distributions have been used to calculate peak discharges with return periods of 5, 10, 25, 50 and 100 years. The calibration and validation of the model is carried out by using data of simulated and observed discharge at the Chunchunkatte gauging station, which shows that the model developed in the present study is accurate. The result of the study shows that Manning’s n ranges between 0.003 and 0.005. For n = 0.005, the performance indices NSE, RMSE and R2 during calibration for the year 2018 are 0.663, 397.061 m3/s and 0.896; validation for the year 2019 is 0.72, 346.621 m3/s and 0.914; and the peak discharge for 100 years return period is 3419.48 m3/s via Gumbel distribution. The output of this study could be useful for flood control authorities to take necessary actions to prevent losses due to floods in the area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Geospatial Techniques for Soil Erosion-Based Watershed Prioritisation: A Review
    (Springer Science and Business Media Deutschland GmbH, 2025) Makhdumi, W.; Suragamallika, R.; Shwetha, H.R.; Dwarakish, G.S.
    The degradation of the environment caused by anthropogenic has raised significant concerns about the sustainability of land, water, and energy resources. It is crucial to acknowledge the unique characteristics of each watershed and the variability in the impact of human and natural activities across regions. Soil erosion emerges as a major threat, which leads to degraded soil, reduced agricultural productivity, and water pollution. Effective watershed management is essential for preventing soil erosion and ensuring the sustainability of resources. A fundamental step in effective watershed management involves evaluating and identifying the most severely impacted sub-watersheds. This study focuses on soil erosion-based prioritisation studies in India, examining their main findings, models, and methodologies. Geospatial techniques, which include Remote Sensing (RS) and Geographic Information System (GIS), have proven effective for mapping and assessing soil erosion at different scales. These methods identify erosion-causing factors, including land use, slope, rainfall intensity, and soil characteristics. By integrating geospatial data, accurate assessments of soil erosion vulnerability can be made, supporting informed decision-making. Multi-Criteria Decision Analysis (MCDA) helps in prioritisation by evaluating multiple soil erosion criteria and assigning weights based on their relative importance. Geospatial tools facilitate comprehensive assessments of soil erosion vulnerability, aiding decision-making processes. The review offers insights for researchers to conduct reliable assessments and generate data on soil erosion. Integrating Land Use Land Cover Changes (LULCC) and socio-economic conditions in prioritisation studies is recommended. This paper can assist researchers generate reliable data on soil erosion, enabling policymakers to make informed decisions regarding adaptation and mitigation strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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    Integrating Soil Spectral Library and PRISMA Data to Estimate Soil Organic Carbon in Crop Lands
    (Institute of Electrical and Electronics Engineers Inc., 2024) Reddy, B.S.; Shwetha, H.R.
    The increasing demand for precise soil organic carbon (SOC) monitoring in croplands is crucial for food security (SDG 2), and has led to the exploration of fusing soil spectral libraries (SSLs) with hyperspectral sensing data for SOC estimation. However, the widespread adoption of SSL for SOC estimation faces challenges, particularly in developing nations, due to inconsistent calibration libraries and reliable estimation models. Furthermore, SSL rely on regular soil sample collection and spectral data recording using spectroradiometers, which is impractical in agricultural-predominant countries, such as India, with limited time for sample collection between crop rotations. To address this challenge, we developed synthesized SSL in laboratory conditions and integrated it with hyperspectral data using machine learning (ML) algorithms to bridge the gap between synthesized SSL and hyperspectral data for local-scale SOC mapping. This approach was tested by mapping SOC in Mysore, India, using spectroradiometer hyperspectral measurements and PRISMA sensor data. The proposed approach and synthesized SSL exhibited better performance prediction accuracies, R2 of 0.92 and 0.79, and the RMSE values of 2.31 and 9.91 g/kg, respectively, for PRISMA and laboratory spectroscopy data. These results highlight the potential of synthesized SSL for SOC prediction in alluvial soils, leveraging local datasets, and hyperspectral data. Our future work will expand the synthesis approach to other study areas, particularly those with alluvial soil origins, further enhancing the applicability of this methodology for SOC estimation and aiding food security efforts. © 2004-2012 IEEE.
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    Kirpich and williams times of concentration in musle: A case study
    (2011) Kamath, M.A.; Varun, V.M.; Dwarakish, G.S.; Kavyashree, B.; Shwetha, H.R.
    Water is one of the most vital requirements for sustenance of life. Water, along with soil, forms the combination of the most essential natural resource for economic and social development. A study of soil and water dynamics at a watershed level can facilitate a scientific approach to a conservation and management plan for them. The present study is an attempt to implement Modified Universal Soil Loss Equation (MUSLE) using Soil Conservation Service Curve Number (SCS-CN) method for runoff estimation and comparison of soil loss results for the catchments of Baindur Hole and Yedamavina Hole in Udupi district of Karnataka state obtained by times of concentration calculated by the Kirpich equation and the Williams equation. Preparation of base map and thematic maps was carried out using IRS-1C, LISS-III image for LU/LC and from SOI toposheet in a GIS environment for overlaying and extraction of results. The time of concentration estimated by the Kirpich formula is lower in all cases; hence the corresponding soil loss is higher by 1.4 times in comparison to Williams' formula. The catchments 23 and 17 with a lower drainage length show comparatively higher values of soil loss in case of the Williams equation which can be attributed to the higher importance given to drainage length in the equation. 2011 Taylor & Francis Group, LLC.
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    Kirpich and williams times of concentration in musle: A case study
    (2011) Kamath, M.A.; Varun, V.M.; Dwarakish, G.S.; Kavyashree, B.; Shwetha, H.R.
    Water is one of the most vital requirements for sustenance of life. Water, along with soil, forms the combination of the most essential natural resource for economic and social development. A study of soil and water dynamics at a watershed level can facilitate a scientific approach to a conservation and management plan for them. The present study is an attempt to implement Modified Universal Soil Loss Equation (MUSLE) using Soil Conservation Service Curve Number (SCS-CN) method for runoff estimation and comparison of soil loss results for the catchments of Baindur Hole and Yedamavina Hole in Udupi district of Karnataka state obtained by times of concentration calculated by the Kirpich equation and the Williams equation. Preparation of base map and thematic maps was carried out using IRS-1C, LISS-III image for LU/LC and from SOI toposheet in a GIS environment for overlaying and extraction of results. The time of concentration estimated by the Kirpich formula is lower in all cases; hence the corresponding soil loss is higher by 1.4 times in comparison to Williams' formula. The catchments 23 and 17 with a lower drainage length show comparatively higher values of soil loss in case of the Williams equation which can be attributed to the higher importance given to drainage length in the equation. © 2011 Taylor & Francis Group, LLC.
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    Quantification and Assessment of The Virtual Water Content of Rice Crop: A Case Study of Mysore District, India
    (Institute of Electrical and Electronics Engineers Inc., 2023) Saicharan, V.; Shwetha, H.R.
    Agriculture is the largest consumer of freshwater among all sectors. Currently, there are minimal studies to quantify a crop's water consumption and virtual water content of crops in India, especially using geospatial products. To address this issue, the current study employed a geospatial approach to quantify and assess the virtual content of rice crop. The actual evapotranspiration, NDVI and crop yield datasets are used in this study to quantify the virtual water content (VWC) of rice crops in the Mysore district from 2015 to 2019. The results show that the rice crop's water consumption (CWC) and VWC are higher in Kharif than in summer. The rice crop yield in Mysore is reducing, but the CWC was increasing with respect to time during the study period. The maximum VWC was observed in the 2018 Kharif season, i.e., 5228.9 m3/ton, and the lowest VWC (962.7 m3/ton) was observed in the summer of 2016. The findings will make it easier to comprehend how much water rice crops need over the course of various seasons and years, allowing for more effective water management. It will also assist officials and water planners in determining which seasons to minimise supply to achieve sustainable water management, especially in arid and semi-arid regions. © 2023 IEEE.
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    Soil erosion in diverse agroecological regions of India: a comprehensive review of USLE-based modelling
    (Springer Science and Business Media Deutschland GmbH, 2023) Makhdumi, W.; Shwetha, H.R.; Dwarakish, G.S.
    Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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    Soil erosion in diverse agroecological regions of India: a comprehensive review of USLE-based modelling
    (Springer Science and Business Media Deutschland GmbH, 2023) Makhdumi, W.; Shwetha, H.R.; Dwarakish, G.S.
    Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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    Soil loss estimation through musle using kirpich and williams times of concentration using rs and gis techniques: A case study
    (2012) Kamath, A.M.; Varun, V.M.; Dwarakish, G.S.; Kavyashree, B.; Shwetha, H.R.
    Water is one of the most vital requirements for sustenance of life. Water, along with soil, is the most essential natural resource for economic and social development. A study of soil and water dynamics at a watershed level can facilitate a scientific approach toward their conservation and management. The present study is an attempt to apply the Modified Universal Soil Loss Equation (MUSLE) along with the Soil Conservation Service Curve Number (SCS-CN) method for runoff estimation and comparison of soil loss estimates for the catchments of Baindur Hole and Yedamavina Hole in the Udupi District of Karnataka State, India, obtained by times of concentration calculated by the Kirpich equation and the Williams equation. The base map and thematic maps were prepared using Indian Remote Sensing satellite 1C (IRS-1C) LISS-III (Linear Imaging Self-Scanning Sensor) image for land use/land cover and from Survey of India topo sheet in a GIS environment for overlaying and extraction of results. The time of concentration estimated by the Kirpich formula is lower in all cases; hence, the corresponding soil loss is 1.4 times higher than that determined with Williams formula. Catchments 23 and 17 with a lower drainage length show comparatively higher values of soil loss in case of the Williams equation, which can be attributed to the greater importance of drainage length in the equation. 2012 Taylor & Francis Group, LLC.
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    Soil loss estimation through musle using kirpich and williams times of concentration using rs and gis techniques: A case study
    (2012) Kamath, A.M.; Varun, V.M.; Dwarakish, G.S.; Kavyashree, B.; Shwetha, H.R.
    Water is one of the most vital requirements for sustenance of life. Water, along with soil, is the most essential natural resource for economic and social development. A study of soil and water dynamics at a watershed level can facilitate a scientific approach toward their conservation and management. The present study is an attempt to apply the Modified Universal Soil Loss Equation (MUSLE) along with the Soil Conservation Service Curve Number (SCS-CN) method for runoff estimation and comparison of soil loss estimates for the catchments of Baindur Hole and Yedamavina Hole in the Udupi District of Karnataka State, India, obtained by times of concentration calculated by the Kirpich equation and the Williams equation. The base map and thematic maps were prepared using Indian Remote Sensing satellite 1C (IRS-1C) LISS-III (Linear Imaging Self-Scanning Sensor) image for land use/land cover and from Survey of India topo sheet in a GIS environment for overlaying and extraction of results. The time of concentration estimated by the Kirpich formula is lower in all cases; hence, the corresponding soil loss is 1.4 times higher than that determined with Williams formula. Catchments 23 and 17 with a lower drainage length show comparatively higher values of soil loss in case of the Williams equation, which can be attributed to the greater importance of drainage length in the equation. © 2012 Taylor & Francis Group, LLC.
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    Spatio-temporal Assessment and Monitoring of Agricultural Drought in Karnataka, India
    (Springer Science and Business Media Deutschland GmbH, 2025) Chandankumar, N.M.; Saicharan, V.; Shwetha, H.R.
    Agricultural drought monitoring is crucial as it affects food production and fodder, especially in countries like India; consequently, it affects the country’s economy, where nearly 70% of the population depends on agriculture for livelihood. Conventional drought indices consider the mean monthly rainfall values to assess the drought conditions by ignoring the intra-monthly rainfall variations. Due to climate change and erratic rainfall patterns, monthly mean values are not a suitable representation of the rainfall that occurred in the corresponding month. To address this challenge, the current study employed a methodology for calculating agricultural drought using a standardized net-precipitation evapotranspiration index (SNEPI) from 2000 to 2022 by accounting for rainfall variations at an intra-monthly scale. This study employed daily gridded rainfall data and monthly evapotranspiration obtained from the India Meteorological Department (IMD) and NASA’s global land data assimilation system (GLDAS), respectively, at 0.25° × 0.25° spatial resolution for the calculation of SNEPI. Intra-monthly variation of rainfall pattern is addressed by deriving the uniformity coefficient and multiplying it with mean monthly rainfall values. The results were compared with the widely used drought index, the standardized precipitation evapotranspiration index (SPEI). The spatial (agroclimatic zones and whole Karnataka level) and temporal (annual and monthly scale) analyses of SNEPI and SPEI were performed. According to the yearly reports of the Karnataka State Natural Disaster Management Centre (KSNDMC), the highest negative rainfall departure occurred in 2003 and 2016, both termed as deficiency periods. The results showed that in 2006, the drought was observed; however, the annual rainfall was near normal magnitude. Therefore, this study presented the detailed results of 2003, 2006, and 2016. A higher magnitude (0.98) of the correlation coefficient was observed for October, the monsoon season’s termination month. Also, the decreased correlations of 0.88, 0.88, and 0.84 were observed for the months of July, August, and September, respectively. This can be interpreted as increased intra-monthly variations, which SNEPI successfully captures, whereas SPEI ignores the variation. SNEPI succeeds in the early detection of drought events due to its ability to detect short-term dry and wet spells, which correlates well with SPEI at all considered months, inferred that it can be used for drought identification. The results suggest that this index is better for understanding the agricultural drought patterns spatially and temporally across Karnataka’s agro-climatic zones, which incorporates the intra-monthly rainfall variations and helps the agricultural community and policymakers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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    SPATIOTEMPORAL ANALYSIS OF SEA SURFACE SALINITY AND SEA SURFACE TEMPERATURE ALONG THE COASTAL REGION OF KARNATAKA
    (Institute of Electrical and Electronics Engineers Inc., 2024) Chandana, A.; Shwetha, H.R.
    This study aims to analyse the spatiotemporal patterns of sea surface salinity (SSS) and sea surface temperature (SST), key parameters affecting the coastal ecosystem of Karnataka, India. The SSS and SST data were obtained from the HYCOM model, a sophisticated oceanographic model that provides high-resolution global ocean data. The analysis of SSS during the monsoon season revealed distinct spatial variations across the studied coastal region, with the most significant increases occurring in Karnataka compared to the west. SST analysis also revealed consistent warming trends in SST across all seasons, particularly during the pre-monsoonal and winter seasons, reflecting reduced freshwater inputs. SSS analysis also reveals a predominance of warming patterns across the area, indicating broader climatic shifts. During the winter season, SSS ranged from 28.45°C to 30.02°C, with 2023 marking the highest mean SST. The spatial distribution and temporal changes in SSS over 30 years provide valuable insights into long-term oceanographic trends and patterns that could be crucial for climate and marine studies. © 2024 IEEE.
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    State-of-the-art hydraulic modelling: a comprehensive review of HEC-RAS for 1D and 2D applications
    (Taylor and Francis Ltd., 2025) Gupta, J.P.; Sahu, M.K.; Dwarakish, G.S.; Shwetha, H.R.
    Hydrodynamic models simplify natural water systems, aiding in water resource management by simulating water movement in various bodies. They are essential for flood forecasting, hazard mapping and decision-making. Despite challenges in predicting floods due to complex terrain and drainage patterns, ongoing research aims to improve model accuracy. This paper reviews state-of-the-art hydrodynamic models, evaluating their application based on parameters like accessibility, time and space discretization. It focuses on one-dimensional (1D) and two-dimensional (2D) models, including coupled 1D–2D models within the HEC-RAS framework, highlighting HEC-RAS as an effective tool for flood modelling. The integration of hydrological and hydraulic models offers a comprehensive approach to flood forecasting and mitigation. This study guides researchers in model selection for specific catchments and assists water resource managers and policymakers by summarizing key hydrodynamic research and sustainable development strategies. © 2025 IAHS.