Browsing by Author "Dodamani, B.M."

Now showing 1 - 20 of 35

A systematic review of performance assessment in canal irrigation systems: Integrating socio-technical, remote sensing, and AI-driven approaches for a climate-resilient future
(University of Mohaghegh Ardabili, 2025) Rajaput, M.; Ramadasa, A.; Dodamani, B.M.
This systematic review investigates the evolution of performance assessment in canal irrigation systems globally, drawing evidence from Asia, Africa, and Latin America. Adhering to PRISMA guidelines, it synthesized 98 peer-reviewed studies and key organizational reports published between 1990 and 2025, primarily from Scopus and Web of Science. The analysis reveals a clear methodological progression from direct measurements to remote sensing (RS) and agro-hydrological modeling, with Artificial Intelligence (AI) now evidenced as an applied tool in some assessments, not merely a prospect. A critical insight, however, is that despite these technical advancements, persistent underperformance is primarily rooted in deep-seated non-technical (financial, institutional, social) barriers. The current review highlights a significant gap: the absence of a unified framework systematically integrating these technical and socio-institutional dimensions with forward-looking climate resilience. Our primary contribution is a novel, integrated socio-technical assessment framework designed to bridge this divide. Distinct from previous reviews, the proposed framework explicitly combines the methodological triad, comprehensive socio-institutional analysis, quantifiable climate resilience metrics, and mechanisms to ensure social equity in AI-driven management. This adaptable, multi-scale diagnostic tool offers an actionable blueprint, applicable from local canal management to national policy levels, that accounts for diverse regional data limitations. By enabling more effective problem diagnosis and intervention design, the proposed framework provides significant analytical value and actionable lessons for enhancing the productivity, equity, and climate resilience of canal irrigation systems, thereby directly advancing Sustainable Development Goals 2 and 6. © Author(s).
An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data
(Taylor and Francis Ltd., 2023) Salma, S.; Keerthana, N.; Dodamani, B.M.
To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Analysis of RVI for rice crops in small-scale agricultural fields using Sentinel-1 SAR data: case study on LAI retrieval using regression algorithms
(Springer Science and Business Media Deutschland GmbH, 2025) Salma, S.; Ket, S.K.; Dodamani, B.M.
Leaf Area Index (LAI) is a crucial indicator for assessing plant growth, canopy structure, photosynthetic capacity, and overall productivity. The Radar Vegetation Index (RVI), a well-established microwave metric, serves as an effective tool for retrieving the LAI due to its sensitivity to vegetation characteristics. The primary objective of utilizing RVI in LAI studies is to improve the accuracy and reliability of LAI estimation, where optical methods may be hindered by atmospheric conditions. Over the past decade, numerous studies have explored the relationship between RVI and LAI, highlighting the potential of RVI for accurate LAI estimation in crops. In particular, for rice crop analysis in this study, the RVI is derived by incorporating the Degree of Polarization (DOP) from a 2 × 2 covariance matrix as the coefficient, along with the polarization backscatter of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data. The study also explores RVI derivation from M-chi (m-?) and M-delta (m-?) decomposition (assuming circularity in dual-polarized data) and linear backscattering intensities. Using the RVI’s, machine learning regression models are applied to retrieve LAI. The DOP over crop period, the temporal analysis of RVI, and in-situ LAI has been employed to examine trends during crop growth. Notably, among all derived RVIs, the one obtained using the DOP technique, particularly when combined with random forest regression, consistently exhibits superior performance for rice crop LAI estimation (R = 0.91; RMSE = 0.25 m2/m2), whereas, the R value for other models ranges a lower value of 0.63 to a higher value of 0.83 with RMSE of higher value 0.64 m2/m2 to a lower value of 0.32 m2/m2. The findings in the study highlights the sensitivity of SAR data to the DOP and the vegetation structure of rice crops in small-scale agricultural fields. © The Author(s), under exclusive licence to the International Society of Paddy and Water Environment Engineering 2024.
ANFIS-based soft computing models for forecasting effective drought index over an arid region of India
(IWA Publishing, 2023) Kikon, A.; Dodamani, B.M.; Deb Barma, S.D.; Naganna, S.R.
Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSO-ANFIS show better performance results with R2 ¼ 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R2 ¼ 0.78. The results are presented suitably with the aid of scatter plots, Taylor’s diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model. © 2023 The Authors.
Application of remotely sensed NDVI and soil moisture to monitor long-term agricultural drought
(2019) Pathak, A.A.; Dodamani, B.M.
The present study aims to assess agricultural drought using remote sensing based NDVI and soil moisture products in a drought prone river basin of India. The study is conducted in the Ghataprabha river basin which is a sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Gridded soil moisture data from Modern-Era Retrospective analysis for Research and Applications (MERRA) from 1980 to 2015 is considered to derive Standardized Soil moisture Index (SSI) at different time scales. The Vegetation Condition Index (VCI) was calculated from MODIS NDVI products from 2000-2013. The results of VCI and SSI indicated significant number of drought episodes during the study period while severe agricultural drought was observed during 2001-2003. A Good agreement between SSI and VCI was observed during drought year. � 2019 SPIE.
Application of remotely sensed NDVI and soil moisture to monitor long-term agricultural drought
(SPIE spie@spie.org, 2019) Pathak, A.A.; Dodamani, B.M.
The present study aims to assess agricultural drought using remote sensing based NDVI and soil moisture products in a drought prone river basin of India. The study is conducted in the Ghataprabha river basin which is a sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Gridded soil moisture data from Modern-Era Retrospective analysis for Research and Applications (MERRA) from 1980 to 2015 is considered to derive Standardized Soil moisture Index (SSI) at different time scales. The Vegetation Condition Index (VCI) was calculated from MODIS NDVI products from 2000-2013. The results of VCI and SSI indicated significant number of drought episodes during the study period while severe agricultural drought was observed during 2001-2003. A Good agreement between SSI and VCI was observed during drought year. Â© 2019 SPIE.
Assessment of agricultural drought by remote sensing technique
(2018) Pathak, A.A.; Dodamani, B.M.
Drought is commonly occurring natural hazard. It has vicious impact on agricultural production as well as on socioeconomic status of an area. Meteorological drought will induce with the deficit of rainfall and leads to agricultural drought as it prolongs. Rainfall is crucial parameter to assess meteorological drought and NDVI based indices can capture agricultural drought satisfactorily. The present study aims to assess meteorological and agricultural drought in the Ghataprabha river basin using Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI). Monitoring of SPI and VCI will benefits to mitigate drought impacts with the proper water resources managements. Ghataprabha river basin is the sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Average annual rainfall of the basin varies from 600 mm to 2000 mm. Gridded rainfall data was procured from the Indian Meteorological Department for the period of forty three years (1970-2013) and considered same as input for SPI. To calculate SPI with multiple time scale, two parameter gamma distribution was implemented. MODIS NDVI products from 2000-2013 was considered for calculation of VCI. Significant number of meteorological drought episodes were observed during the study period while severe agricultural drought was observed during 2001-2003 and in 2012. SPI and VCI were compared to quantify variation of VCI with respect to SPI. Good agreement between SPI and VCI was observed during drought and non-drought periods. Results indicates that eastern part of the basin was more prone to severe droughts as compare to other part of the basin. This study assistances to formulate drought mitigation strategies and to establish effective water resources policies in the study region. � SPIE. Downloading of the abstract is permitted for personal use only.
Assessment of agricultural drought by remote sensing technique
(SPIE spie@spie.org, 2018) Pathak, A.A.; Dodamani, B.M.
Drought is commonly occurring natural hazard. It has vicious impact on agricultural production as well as on socioeconomic status of an area. Meteorological drought will induce with the deficit of rainfall and leads to agricultural drought as it prolongs. Rainfall is crucial parameter to assess meteorological drought and NDVI based indices can capture agricultural drought satisfactorily. The present study aims to assess meteorological and agricultural drought in the Ghataprabha river basin using Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI). Monitoring of SPI and VCI will benefits to mitigate drought impacts with the proper water resources managements. Ghataprabha river basin is the sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Average annual rainfall of the basin varies from 600 mm to 2000 mm. Gridded rainfall data was procured from the Indian Meteorological Department for the period of forty three years (1970-2013) and considered same as input for SPI. To calculate SPI with multiple time scale, two parameter gamma distribution was implemented. MODIS NDVI products from 2000-2013 was considered for calculation of VCI. Significant number of meteorological drought episodes were observed during the study period while severe agricultural drought was observed during 2001-2003 and in 2012. SPI and VCI were compared to quantify variation of VCI with respect to SPI. Good agreement between SPI and VCI was observed during drought and non-drought periods. Results indicates that eastern part of the basin was more prone to severe droughts as compare to other part of the basin. This study assistances to formulate drought mitigation strategies and to establish effective water resources policies in the study region. Â© SPIE. Downloading of the abstract is permitted for personal use only.
Assessment of meteorological drought return periods over a temporal rainfall change
(Springer Science and Business Media Deutschland GmbH, 2021) Datta, R.; Pathak, A.A.; Dodamani, B.M.
Investigation of the rainfall homogeneity along with bivariate frequency analysis of drought considering change points in long-term annual precipitation series has been carried out in this study. Nonparametric Pettittâ€™s test was applied for detecting change points of annual precipitation series at different grid locations over the Ghataprabha River Basin. Depending on the results of change point analysis, we divided the entire period of 1950â€“2013 into two subperiods: from 1950 to 1980 and 1981 to 2013. Characterization of meteorological drought is performed with the help of the Standardized Precipitation Index (SPI) at a time scale of three months for the period before the change point (1950â€“1980), after the change point (1981â€“2013) and for the entire period of 1950â€“2013. Three Archimedean copulas, namely Clayton, Gumbelâ€“Houggard, and Frank, were tested for joint distribution modeling. The Akaikeâ€™s and Bayesian information criteria have been implemented for selecting the best copula; the Gumbelâ€“Hougaard copula performed comparatively better for all three periods. Drought return periods were calculated using the joint distribution of drought characteristics. The study gives valuable insight into drought risk management on a regional scale. Â© Springer Nature Singapore Pte Ltd 2021.
Comparison of Meteorological Drought Indices for Different Climatic Regions of an Indian River Basin
(Korean Meteorological Society komes@komes.or.kr, 2020) Pathak, A.A.; Dodamani, B.M.
Droughts being a regional phenomenon has a vicious impact on agricultural production as well as on the socioeconomic status of an area. Meteorological drought is not only the result of rainfall deficit but also influenced by temperature in the form of evapotranspiration. There are several indices that could assess meteorological drought. Because of the complex phenomenon underling in the interaction between climatic, hydrological and ecological variables hampers to ascertain the suitability of a drought index to a particular region. The present work aims to compare different meteorological drought indices for a given climatic condition at the regional level. The Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Standardized Precipitation Evapotranspiration Index (SPEI) were employed to study the variation of drought characteristics calculated from these indices. The study was implemented in the Ghataprabha river basin, which is one of the potential lands for agriculture in the basin of river Krishna. The study area possesses negative trends in rainfall and significant increasing trends in the temperature when tested with the Mann-Kendell trend test. Several drought events were observed through SPI, RDI, and SPEI over the basin. SPEI identified the highest number of drought events with high duration and severe intensity as compared to SPI and RDI. The alike performance was noticed between RDI and SPI whereas SPEI does not harmonize with them at any timescale of the study period. The study recommends to consider RDI and SPI in the humid (subhumid) region and SPEI at the semiarid (arid) region to assess the impact of drought effectively. The study also suggests to use an appropriate drought index for analysis of drought, which could lead to an adequate preparedness for the future drought hazards. © 2019, Korean Meteorological Society and Springer Nature B.V.
Connection between Meteorological and Groundwater Drought with Copula-Based Bivariate Frequency Analysis
(American Society of Civil Engineers (ASCE), 2021) Pathak, A.A.; Dodamani, B.M.
Groundwater is a major resource of freshwater that provides additional resilience to agricultural drought during rainfall deficit and also helps in understanding the nature of the hydrological drought risk of an area. This study investigated the response of groundwater drought to meteorological drought and local aquifer properties by considering monthly groundwater levels of a tropical river basin in India. Further, bivariate frequency analysis was carried out for groundwater drought to develop severity-duration-frequency curves by considering the copula function. Long-term monthly groundwater levels were procured, and cluster analysis was performed on groundwater observations to classify the wells. Standardized Groundwater level Index (SGI) was used to evaluate groundwater drought for each cluster, and the same was compared with the meteorological drought of different association periods. The cluster analysis conveyed that wells can be grouped into three clusters optimally. Based on the comparison of groundwater drought with meteorological drought, it was inferred that SGI is well harmonized with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) in humid and semiarid regions, respectively. Analysis of hydraulic diffusivity with the autocorrelation structure of SGI emphasizes the crucial role of aquifer characteristics in local groundwater droughts. The results of joint and conditional return periods obtained from bivariate frequency analysis conveyed that high severity and high-duration droughts were more frequent in the well of Clusters 1 as well as Cluster 3 and comparatively less for the well of Cluster 2. The outcome of the study will be helpful to design proactive drought mitigation and preparedness strategies by considering conjunctive use of surface and groundwater. It also provides a framework to evaluate groundwater drought risk in other parts of the world. © 2021 American Society of Civil Engineers.
Drought monitoring for RABI season in upper Krishna river basin using remote sensing and GIS
(2015) Chandran, C.; Dodamani, B.M.; Reddy, K.; Naseela, E.K.
In this study, the upper Krishna river basin, lying in the state of Maharashtra has been chosen as study area. Two drought indices, SPI and NDVI, representing meteorological and agricultural droughts respectively, were calculated and analysed for the study area for a study period of 2000-2012. Using ArcGIS maps of the two types of droughts have been created to represent the spatial extent of the droughts. Further analysing the two indices, relevant relationships have been obtained between them.
Drought monitoring for RABI season in upper Krishna river basin using remote sensing and GIS
(Asian Association on Remote Sensing Sh1939murai@nifty.com, 2015) Chandran, C.; Dodamani, B.M.; Reddy, K.; Naseela, E.K.
In this study, the upper Krishna river basin, lying in the state of Maharashtra has been chosen as study area. Two drought indices, SPI and NDVI, representing meteorological and agricultural droughts respectively, were calculated and analysed for the study area for a study period of 2000-2012. Using ArcGIS maps of the two types of droughts have been created to represent the spatial extent of the droughts. Further analysing the two indices, relevant relationships have been obtained between them.
Effect of disturbed river sediment supply on shoreline configuration: A case study
(Taylor and Francis Ltd., 2022) Yadav, A.; Dodamani, B.M.; Dwarakish, G.S.
The magnitude of river sediment supply and its distribution play a significant role in coastal sediment dynamics, especially in erosion and deposition. Due to the construction of the dam, obstruction in the natural flow of water occurs, and part of the sediment is trapped. In the present study, the Kali river catchment and its river-mouth at Karwar, Devbagh, and Ravindranath Tagore beaches are considered as the study area, to assess the impact of dams on coastal processes. Landsat data for 42 years, from 1975 to 2017, were collected and analyzed using DSAS, an ArcGIS extension. The sediment yield estimated at the Kali river basin outlet, without the dam is 4.19 t/ha/yr and with the dam, it is estimated to be 1.42 t/ha/yr. Similarly, for the Aghanashini river basin outlet, the sediment yield was found to be 4.58 t/hr/yr. From the results of shoreline analysis, it is found that after the construction of the dam, Devbagh beach is under erosion at the rate of ?0.93 m/yr End Point Rate (EPR) and ?0.47 m/yr Linear Regression Rate (LRR). Ravindranath Tagore beach also has undergone erosion, which is ?0.75 m/yr (EPR) and ?0.97 m/yr (LRR). Further, both the beaches have been changed to the erosion zone. © 2021 Indian Society for Hydraulics.
Effect of the drag coefficient on the performance of vertical porous baffles in a sloshing tank
(Emerald Publishing, 2025) Bhandiwad, M.S.; Dodamani, B.M.; Deepak, D.
Purpose: The present work involves analytical and experimental investigation of sloshing in a two-dimensional rectangular tank including the effect of porous baffles to control and/or reduce the wave motion in the sloshing tank. The purpose of this study is to assess the analytical solutions of the drag coefficient effect on porous baffles performance to track free surface motion variation in the sloshing tank by comparison with experimental shake table tests under a range of sway excitation. Design/methodology/approach: The linear second-order ordinary differential equations for liquid sloshing in the rectangular tank were solved using Newmark’s beta method and obtained the analytical solutions for liquid sloshing with dual vertical porous baffles of full submergence depths in a sway-oscillated rectangular tank following the methodology similar to Warnitchai and Pinkaew (1998) and Tait (2008). Findings: The porous baffles significantly reduce wave elevation in the varying filled levels of the tank compared to the baffle-free tank under the range of excitation frequencies. It is observed that the Reynolds number-dependent drag coefficient for porous baffles in the tank can significantly reduce the sloshing elevations and is found to be effective to achieve higher damping compared to the porosity-dependent drag coefficient for porous baffles in the sloshing tank. The analytical model’s response to free surface elevation variations in the sloshing tank was compared with the experiment’s test results. The analytical results matched with shake table test results with a quantitative difference near the first resonant frequency. Research limitations/implications: The scope of the study is limited to porous baffles performance under range sway motion and three different filling levels in the tank. The porous baffle performance includes Reynolds number dependent drag coefficient to explore the damping effect in the sloshing tank. Originality/value: The porous baffles with low-level porosities in the sloshing tank have many engineering applications where the first resonant mode of sloshing in the tank is more important. The porous baffle drag coefficient is an important parameter to study the baffle’s damping effect in sloshing tanks. Hence, obtained analytical solution for liquid sloshing in the rectangular tank with Reynolds number as well as porosity-dependent drag coefficient (model 1) and porosity-dependent drag coefficient porous baffles (model 2) performance is discussed. The model’s test results were validated using a series of shake table sloshing experiments for three fill levels in the tank with sway motion at various excitation frequencies covering the first four sloshing resonant modes. © 2023, Emerald Publishing Limited.
Hybrid Deep Learning-Based Potato andÂ Tomato Leaf Disease Classification
(Springer Science and Business Media Deutschland GmbH, 2024) Patil, M.A.; Manur, M.; Laxuman, C.; Parane, K.; Dodamani, B.M.; Sunkad, G.
Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Identifying Municipal Solid Waste Dumping Site Location Using AHP and GIS Techniques: A Case Study of Coimbatore District, India
(Springer, 2022) Aishwarya, V.; Salma, S.; Dodamani, B.M.
Increased municipal solid waste generation in urban areas is a result of fast population growth and urbanization. Dumping or landfilling in unsuitable areas becomes the biggest concern for solid waste management authorities. The present dump yard at Vellalore, Coimbatore district, affect nearby settlements with a foul stench and flying ashes due to strong winds. The study’s main goal was to provide alternative landfilling sites in the Coimbatore district using GIS and analytic hierarchy process (AHP) techniques. Nine criteria were considered. These were population density, slope, geology, geomorphology, land use/land cover, and proximity to road, river, railway, and airport. Weighted overlay, a spatial analyst tool that reclassifies raster maps and a final suitability map, is generated. According to the findings, the possible landfill zones were found in the northeastern region of Coimbatore. Hence, the environmentally suitable sites can be selected by using remote sensing and GIS techniques. © 2022, Indian Society of Remote Sensing.
Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data
(Springer, 2022) Keerthana, N.; Salma, S.; Dodamani, B.M.
In India, the majority of the population relies heavily on rice as it is their primary source of nutrition. Rice crop yield productivity depends on seasonal variations and mainly depends on hydrological conditions. Long-term water clogging in rice fields for an extended period causes crop flooding and reduces production in terms of quality and quantity. This study deals with the identification of rice crop fields and their flooding due to surface irrigation using Sentinel-1 SAR data. The identification of rice fields was attempted by classifying the image data using a random forest algorithm. For crop flooding analysis, the temporal backscatter of the corresponding fields has been extracted from SAR data and local thresholding is used. The temporal analysis of the SAR backscattering showed a similar tendency in terms of crop growth. The overall accuracy of rice crop classification for VH and VV is 97.30% and 92.24% with RMSE errors of 0.0143 and 0.0145, respectively, obtained at the peak stage of the crop. From the crop flooding analysis, it is observed that crop fields have been flooded at the growth stage due to surface irrigation and rainfall. We identified crop flooding even at the crop mature stage. In the analysis, it has been observed that the flooding is not due to irrigation water but is due to the precipitation water. © 2022, Indian Society of Remote Sensing.
Impacts of dams on sediment yield and coastal processes using swat and dsas tools
(Springer Science and Business Media Deutschland GmbH, 2021) Athira, K.; Yadav, A.; Dodamani, B.M.; Dwarakish, G.S.
Soil erosion is considered as one of the major causes of land degradation and reservoir sedimentation. Therefore, modeling of runoff and sediment yield at the catchment level is necessary. In this study, an attempt was made to simulate runoff and sediment yield of hydrologically similar basins of Kali River and Aghanashini River which joins west coast of India. A conceptual, continuous time and semi-distributed SWAT2012 (Soil and Water Assessment Tool) model was selected for the modeling purpose. For the last two decades, Kali river basin experienced a very high rate of soil erosion due to various developmental activities in the basin. Therefore, it is essential to identify the soil loss within the basin. There are five dams constructed across the Kali river basin for various purposes. The presence of these reservoirs regulates stream flow and thus sediment load in the basin. However, the free movement of water across the Aghanashini river catchment leads to the unobstructed passage of sediments to the river mouth, as the catchment is not disturbed by the reservoir. This study deals with the impacts of the dams on stream flow, sediment load and the response of shoreline. Digital Shoreline Analysis System (DSAS) tool was used to analyze the shoreline changes. Simulated and observed values of runoff are compared, and calibration and validation were done for the basins using SWAT-CUP. Analysis of calibration and validation results shows that the model has a good performance. Therefore, the SWAT model can be used to conduct further studies in these study areas. Sediment yield obtained at the catchment outlet was 1.07Â t/ha/year and 4.58Â t/ha/year for Kali and Aghanashini basins, respectively. Less amount of sediment load in the Kali basin indicate the influence of reservoir operation on stream flow and sediment yield. The shoreline analysis of both the basins concluded that Devbagh beach connecting with Kali river estuary is under erosion and Aghanashini beach is under naturally nourished condition. Â© Springer Nature Singapore Pte Ltd 2021.
Nonlinear analysis of groundwater levels: investigating trends and the impact of El Niño on groundwater drought in a southern region of India
(Springer Science and Business Media Deutschland GmbH, 2025) Poojitha, K.; Dodamani, B.M.
The expansion of groundwater irrigation and the cultivation of water-intensive sugarcane, combined with low rainfall, have exacerbated groundwater depletion and intensified droughts in the semi-arid Upper Krishna basin, India. This study employs an iterative singular spectrum analysis (iterative SSA) approach to impute missing groundwater level data from 25 monitoring wells. Cross-validation results show that iterative SSA effectively preserves the overall data structure when missing data was random, achieving good performance metrics with NSE > 0.79, R2 > 0.8 and RMSE < 0.88 under optimal parameters (L = 12 and k = 5). The reconstructed groundwater levels were then used to identify nonlinear trends with a 180-month smoothing SSA window and to investigate the impact of strong El Niño events on groundwater drought through cross-wavelet transform (XWT) and wavelet coherence (WTC) analyses between 1983 and 2017. The nonlinear trends revealed short-term deviations in groundwater levels during 1991–2000, 2002–2003, and 2015–2017. These deviations were corroborated by significant cross-wavelet power and high wavelet coherence between the Niño 3.4 SST Index and groundwater drought, particularly under low rainfall conditions, indicating stress on the region’s groundwater system. Although the study effectively captures the nonlinear nature of groundwater levels and the influence of climate variability on drought, the complexity of the groundwater system in the region persists due to physical water scarcity and high groundwater extraction for irrigation. This study highlights the importance of imputing missing data and applying nonlinear trend and wavelet analyses to detect short-term deviations caused by severe droughts, driven by strong El Niño events and high irrigation demands. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.