Browsing by Author "Varija, K."

Now showing 1 - 13 of 13

Assessing the changing pattern of hydro-climatic variables in the Aghanashini River watershed, India
(Springer Science and Business Media Deutschland GmbH, 2023) Yashas Kumar, H.K.; Varija, K.
Growing population and climate change have altered the hydro-climatic trend from past decades. This manuscript analyses the abrupt shift in these time series and their changing pattern using historical data sets. The Pettitt test and the Standard Normal Homogeneity Test were used to evaluate the time series' homogeneity. The Concentration Index, Precipitation Concentration Index and Seasonality Index were employed to analyse the spatial variability of daily, monthly and seasonal rainfall patterns over the Aghanashini River watershed. Furthermore, the temporal trend in the rainfall, streamflow, and temperature time series was investigated using Mann–Kendall (MK) and the graphical Innovative-Şen (IŞ) test. Clear evidence of climate change impact on the rainfall and streamflow pattern was recognized, as there is an upward shift in the maximum temperature time series and a downward shift in the rainfall and streamflow time series after 2001. The rainfall indices showed that the watershed has fewer percentage of rainy days and stronger rainfall seasonality, indicating a possible risk of flash floods in the downstream of the watershed. Additionally, the results of the MK and IŞ trend tests paralleled each other and provided support for the findings emphasized by rainfall indices. © 2023, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.
Identification of the best-fit probability distribution and modeling short-duration intensity-duration-frequency curvesâ€”mangalore
(Springer Science and Business Media Deutschland GmbH, 2021) Femin, C.V.; Varija, K.
The study of frequency analysis is crucial to find the most fitting model that could predict extreme events of certain natural phenomena, e.g., rainfall, flood. The study aims to determine the best-fit probability distribution model for maximum daily rainfall of four stations in Mangalore city. Statistical analysis such as log-normal, log-Pearson, and generalized extreme value (GEV) was applied, and parameters of these distributions were estimated. The predicted values using these distributions subjected to the goodness of fit test using the Kolmogorovâ€“Smirnov test, Andersonâ€“Darling test, and Chi-squared test. Generalized extreme value distribution gave the best-fit model and thus, used for deriving the intensity duration frequency (IDF) curves for Mangalore city. IDF curves using empirical equation and GEV distribution were compared, and GEV distribution IDF curves give higher rainfall intensities. Â© Springer Nature Singapore Pte Ltd 2021.
Improved vegetation parameterization for hydrological model and assessment of land cover change impacts on flow regime of the Upper Bhima basin, India
(2018) Mohaideen, M.M.D.; Varija, K.
This study investigates the potential and applicability of variable infiltration capacity (VIC) hydrological model to simulate different hydrological components of the Upper Bhima basin under two different Land Use Land Cover (LULC) (the year 2000 and 2010) conditions. The total drainage area of the basin was discretized into 1694 grids of about 5.5 km by 5.5 km: accordingly the model parameters were calibrated at each grid level. Vegetation parameters for the model were prepared using temporal profile of Leaf Area Index (LAI) from Moderate-Resolution Imaging Spectroradiometer and LULC. This practice provides a methodological framework for the improved vegetation parameterization along with region-specific condition for the model simulation. The calibrated and validated model was run using the two LULC conditions separately with the same observed meteorological forcing (1996 2001) and soil data. The change in LULC has resulted to an increase in the average annual evapotranspiration over the basin by 7.8%, while the average annual surface runoff and baseflow decreased by 18.86 and 5.83%, respectively. The variability in hydrological components and the spatial variation of each component attributed to LULC were assessed at the basin grid level. It was observed that 80% of the basin grids showed an increase in evapotranspiration (ET) (maximum of 292 mm). While the majority of the grids showed a decrease in surface runoff and baseflow, some of the grids showed an increase (i.e. 21 and 15% of total grids surface runoff and baseflow, respectively). 2018, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.
Improved vegetation parameterization for hydrological model and assessment of land cover change impacts on flow regime of the Upper Bhima basin, India
(Springer International Publishing kasia@cesj.com, 2018) Mohaideen, M.M.D.; Varija, K.
This study investigates the potential and applicability of variable infiltration capacity (VIC) hydrological model to simulate different hydrological components of the Upper Bhima basin under two different Land Use Land Cover (LULC) (the year 2000 and 2010) conditions. The total drainage area of the basin was discretized into 1694 grids of about 5.5 km by 5.5 km: accordingly the model parameters were calibrated at each grid level. Vegetation parameters for the model were prepared using temporal profile of Leaf Area Index (LAI) from Moderate-Resolution Imaging Spectroradiometer and LULC. This practice provides a methodological framework for the improved vegetation parameterization along with region-specific condition for the model simulation. The calibrated and validated model was run using the two LULC conditions separately with the same observed meteorological forcing (1996–2001) and soil data. The change in LULC has resulted to an increase in the average annual evapotranspiration over the basin by 7.8%, while the average annual surface runoff and baseflow decreased by 18.86 and 5.83%, respectively. The variability in hydrological components and the spatial variation of each component attributed to LULC were assessed at the basin grid level. It was observed that 80% of the basin grids showed an increase in evapotranspiration (ET) (maximum of 292 mm). While the majority of the grids showed a decrease in surface runoff and baseflow, some of the grids showed an increase (i.e. 21 and 15% of total grids—surface runoff and baseflow, respectively). © 2018, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.
Interception Loss from the Sprinkler-Irrigated Coffee Plantation
(Springer Nature, 2022) Narayana, P.; Varija, K.
Evaporation losses for two sets of experiments were carried out during a hot summer day. To measure the evaporation losses, pairs of funnel-type rain gauges were used in the interspace between plants and below the canopy to measure rainfall and through-fall, respectively, at an equal distance from the jet. Water depths under two types of sprinkler irrigation jets with varying discharge rates were measured. The experiment has been done close to the same operating conditions. The results showed that evaporation losses were 29.6 and 21.1 per cent, respectively, for the two sets of experiments. The losses are comparable to the air temperature and vapour pressure deficit. Water loss due to drift has not been considered in the present study due to negligible wind speed on the day of the experiment and windbreaks from the shading trees. During strong winds, the actual losses will be higher than the measured experimental values. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Machine learning–based assessment of long-term climate variability of Kerala
(Springer Science and Business Media Deutschland GmbH, 2022) Vijay, A.; Varija, K.
Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119 years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann–Kendall test (MK), three modified Mann–Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70 mm and 28.30 mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20 °C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Revitalizing temperature records: A novel framework towards continuous data reconstruction using univariate and multivariate imputation techniques
(Elsevier Ltd, 2024) Yashas Kumar, H.K.; Varija, K.
Data gaps are a recurring challenge in climate research, hindering effective time series analysis and modeling. This study proposes a novel two-step data imputation framework to address temperature time series with a long continuous gap surrounded by predictor stations with sporadic missingness. The method leverages iterative gap-filling Singular Spectrum Analysis (SSA) for the small sporadic gaps, followed by multivariate techniques like Inverse Distance Weightage (IDW), Kriging, Spatial Regression Test (SRT), Point Estimation method of Biased Sentinel Hospital-based Area Disease Estimation (P-BSHADE), Random Forest (RF), Support Vector Machines (SVM), and MissForest (MF) for the longer gap. Once the sporadic gaps are effectively addressed with SSA, the method carefully applies multivariate techniques to impute the long continuous gap. Prioritizing accuracy, comprehensive cross-validation with class-based statistical indicators are employed to minimize any potential biases introduced by the imputation process. The study shows the effectiveness of SSA in filling small sporadic gaps using an optimal window length (M ? 365 days) and eigentriple grouping (ET = 30). Notably, for maximum temperature, P-BSHADE and SVM achieve an impressive accuracy (e.g., Legates's Coefficient of Efficiency (LCE), 0.75?0.44, Combined Performance Index (CPI), 6.3%?19.1%) attributed to their ability to capture spatial and/or temporal heterogeneity. While SRT and P-BSHADE offers acceptable performance for minimum temperature (e.g., LCE, 0.51?0.27, CPI, 0.7%?23.7%), the study also uncovers a complex interplay between missing data, predictor stations, and autocorrelation affecting imputation accuracy. This suggests that the reduced performance of certain techniques likely stems from the decline in spatial and spatiotemporal autocorrelation between the target station and its predictors. Overall, this study presents a promising framework for handling complex missing data scenarios often encountered in climate time series analysis, paving the way for more robust and reliable analysis and modeling. © 2024 Elsevier B.V.
Soil Moisture Variability and Hydrological Impact Assessment of Land Cover Change
(National Institute of Technology Karnataka, Surathkal, 2020) M, Diwan Mohaideen M.; Varija, K.
Water availability in a region depends on how precipitation over the region is transformed into various forms after reaching ground such as evaporation losses, runoff, infiltration, soil moisture, and ground water storage etc. Land Use / Land Cover (LU/LC) changes adversely affect the aforementioned components. Particularly, the effects of LU/LC changes on catchment hydrological responses, especially vegetative cover (forest, scrubs and cropland), affect the evapotranspiration. Further, rapid urbanization due to LU/LC changes leads to extent of impervious surface and thereby, impacts the infiltration rates as well as recharge. The LU/LC change impact on the hydrologic system is region specific, and each region is characterized by its own hydrology, terrain, climate and also anthropogenic factors. Therefore, a detailed assessment of LU/LC change impacts on hydrology is required, specifically at the region with seasonally limited water availability. It is emphasized by many researchers that the physically-based, distributed hydrological models along with remote sensing capabilities are more suitable for assessing the LU/LC change impacts on the hydrologic system. Further, Soil moisture, being a critical state variable, its knowledge is of paramount importance in several hydrological applications (e.g., runoff modeling and flood forecasting, agricultural monitoring and drought monitoring). The magnitude of soil moisture variability often under estimated and the spatial pattern of soil moisture is not consistent, and it is largely varying across the site and climate with the influence of heterogeneity in LU/LC, topography, soil properties, precipitation and evapotranspiration. Hence, the characterization of soil moisture variability is essential. The work reported in this thesis aims at understanding the soil moisture variability and land cover change impacts in an agricultural dominant semi-arid basin. The Variable Infiltration Capacity (VIC) model, a physically based, semi distributed hydrological model was used to simulate the hydrologic responses of the basin for different LU/LC scenarios (the year 2000 and 2010) with multiple soil layersiv parameterization (three soil layers: 0 – 10 cm, 10 – 45 cm and 45 – 100 cm). The total drainage area of the basin was discretized into number of model grids (5.5 km resolution: totally 1694 grids), and the input parameterization of the model was made at each grid level. The major input parameters to the model are meteorological forcing (Precipitation, Tmax, Tmin), soil characteristics, land surface vegetation classes (vegetation parameter & library) and topography. This study demonstrated a methodological frame work for improved vegetation parameterization to the model simulation. Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 8-day Lear Area Inded (LAI) time-series data was used to sub-group agricultural dominant areas into major crop groups and corresponding monthly vegetation phenology in terms of LAI, albedo, height, root distribution were arrived. This exercise enabled improved definition of vegetation parameterization for the study area, incorporating the region specific conditions. Firstly, the model was calibrated and validated using the observed stream flow data collected at two different locations for the period 1994 – 2001. The model parameter values were adopted for each model grid (about 5.5 km) based on the saturated hydraulic conductivity at that grid by trial and error method. To assess the hydrological impacts of LU/LC change on the flow regime of the basin, the model was run using the two LU/LC conditions separately with the same observed meteorological forcing and soil data. The changes attributed to LU/LC at basin level indicate that the surface runoff and baseflow decreased by 18.86 and 5.83% respectively. The evapotranspiration increased by 7.8%, mainly because of the agricultural crops. The variability in hydrological components and the spatial variation of each component attributed to LU/LC was further assessed at the basin grid level. The majority of the basin grids showed an increase in evapotranspiration (80 % of basin grids) and subsequent decrease in runoff and baseflow (79 and 85% of basin grids, respectively) with resepect to LU/LC change. Further, the spatio-temporal variation of soil moisture was assessed using the model simulated soil moisture along with three different satellite derived surface soil moisture products (SM-CCI, SM-TRMM and SM-AMSRE). It was found from the analysis that the impacts of LU/LC changes on soil moisture were more evident in the deeper layers (45 cm and 100 cm). The soil moisture decreased byv an average of 14.43 and 18.21% (percentage change), particularly in dry periods at second and third layers, respectively. Further, the modeled soil moisture along with three different satellite surface soil moisture products were investigated for its spatio-temporal variability in the basin. The soil moisture in the top layer (up to 10 cm) showed high temporal variations. However, the mean soil moisture was found almost constant during the summer and winter seasons. The basin showed high variability in soil moisture during the intermediate wetness condition. Further, the spatial variability of the soil moisture during the wetting period (June-August) was high compared to drying period (December – February). Based on the analysis performed in this study, 29 (out of total model grids - 1694) representative grid locations were identified in the basin. These locations could be effectively considered for installing observational network mainly for monitoring soil moisture in near real-time.
Soil water-retention prediction from pedotransfer functions for some Indian soils
(2013) Shwetha, P.; Varija, K.
This paper discusses the development of pedotransfer functions (PTFs) and uses a multiple nonlinear regression technique to validate point and parametric PTFs for the estimation of a water-retention curve from basic soil properties such as particle-size distribution, bulk density and organic matter content. One hundred soil samples were collected at different depths from different locations in the Pavanje river basin that lies within the southern coastal region of Karnataka, India. Prediction accuracies were evaluated using the coefficient of determination (R 2), root mean square error (RMSE) and mean error (ME) between measured and predicted values. Overall, both point and parametric methods predicted water contents at selected water potentials with considerable accuracy. However, prediction of the soil water-retention curve using PTFs by point estimation method was relatively more successful (best case R 2 = 0.983) for the sampled soils. F-tests were also conducted for all cases. For one regression equation, the p-value was zero and for other equation, values were close to zero. Critical comparative analysis was carried out on the performances of the point and parametric methods. Use of the developed PTFs is suggested for the prediction of a water-retention curve for loamy sand and sandy loam soils in this area of the coastal region of southern India. 2013 Copyright Taylor and Francis Group, LLC.
Soil water-retention prediction from pedotransfer functions for some Indian soils
(2013) Prasanna, P.; Varija, K.
This paper discusses the development of pedotransfer functions (PTFs) and uses a multiple nonlinear regression technique to validate point and parametric PTFs for the estimation of a water-retention curve from basic soil properties such as particle-size distribution, bulk density and organic matter content. One hundred soil samples were collected at different depths from different locations in the Pavanje river basin that lies within the southern coastal region of Karnataka, India. Prediction accuracies were evaluated using the coefficient of determination (R 2), root mean square error (RMSE) and mean error (ME) between measured and predicted values. Overall, both point and parametric methods predicted water contents at selected water potentials with considerable accuracy. However, prediction of the soil water-retention curve using PTFs by point estimation method was relatively more successful (best case R 2 = 0.983) for the sampled soils. F-tests were also conducted for all cases. For one regression equation, the p-value was zero and for other equation, values were close to zero. Critical comparative analysis was carried out on the performances of the point and parametric methods. Use of the developed PTFs is suggested for the prediction of a water-retention curve for loamy sand and sandy loam soils in this area of the coastal region of southern India. © 2013 Copyright Taylor and Francis Group, LLC.
Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach
(Springer Science and Business Media Deutschland GmbH, 2024) Vijay, A.; Varija, K.
Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Study on soil moisture retention function for the indian forested hillslope soils
(2013) Shwetha, P.; Varija, K.; Kumar, P.
Pedotransfer functions (PTFs) are one of the widely used tools to predict the soil water retention curves (SWRC). The objective of this study was to develop and validate point and parametric PTF models based on nonlinear regression technique using the different set of predictors such as particle-size distribution, bulk density, porosity and organic matter content. Soil samples were collected from different elevations at different depths in forested hillslope area of Pavanje river basin that lies in coastal area of Karnataka, India. The point PTF models estimated retention points at 33, 100, 300, 500, 1000, and 1500 kPa pressure heads and the parametric PTF models estimated the van Genuchten and Brooks-Corey retention parameters. The data were evaluated with the root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) between the measured and predicted water contents. The prediction of soil water retention curve using PTFs by point estimation method for the sampled soils was relatively successful (best case R2 = 0.862). Further, a critical comparative analysis on the performances of point and parametric methods was done. It can be suggested to use the developed PTFs for the prediction of soil water retention curve for the loamy sand and sandy loam textured soils in this forest area of the coastal region in south western portion of India.
Study on soil moisture retention function for the indian forested hillslope soils
(Taiwan Geotechnical Society 43, Sec. 4, Keelong Rd, Taipei 106,, 2013) Prasanna, P.; Varija, K.; Kumar, P.
Pedotransfer functions (PTFs) are one of the widely used tools to predict the soil water retention curves (SWRC). The objective of this study was to develop and validate point and parametric PTF models based on nonlinear regression technique using the different set of predictors such as particle-size distribution, bulk density, porosity and organic matter content. Soil samples were collected from different elevations at different depths in forested hillslope area of Pavanje river basin that lies in coastal area of Karnataka, India. The point PTF models estimated retention points at 33, 100, 300, 500, 1000, and 1500 kPa pressure heads and the parametric PTF models estimated the van Genuchten and Brooks-Corey retention parameters. The data were evaluated with the root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) between the measured and predicted water contents. The prediction of soil water retention curve using PTFs by point estimation method for the sampled soils was relatively successful (best case R2 = 0.862). Further, a critical comparative analysis on the performances of point and parametric methods was done. It can be suggested to use the developed PTFs for the prediction of soil water retention curve for the loamy sand and sandy loam textured soils in this forest area of the coastal region in south western portion of India.