Browsing by Author "Mahesha, A."

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A multivariate index-flood approach for flood frequency analysis of ungauged watersheds: a case study on state of Kerala in India
(Springer Science and Business Media Deutschland GmbH, 2025) HariKrishna, M.; Vinod, D.; Desai, S.; Mahesha, A.
The multivariate index-flood method (MIF) advances flood risk evaluation at ungauged watersheds by utilizing information from gauged sites within a uniform region to forecast flood attributes where direct data is absent. It aims to enhance flood frequency analysis at ungauged watersheds by considering the interdependence between multiple flood variables using copulas and multivariate quantile curves. The proposed methodology involves screening data for anomalies, delineating homogeneous regions based on physiographic and hydrological descriptors, and selecting appropriate regional marginal distributions and copulas. Regional Flood Frequency Analysis and the index-flood method, MIF, can produce dependable multivariate quantile approximations, enhancing the precision of flood projections and risk evaluations at ungauged watersheds. Nine watersheds in the Indian state of Kerala situated along rivers flowing westward have been subjected to the suggested multivariate technique, which focuses on the bivariate case. This implementation involves recorded data series on flood volume and peak flow. The dataset includes daily maximum discharge data from India-WRIS, gridded precipitation and temperature data from IMD, and a 30 × 30 m DEM from USGS SRTM. The data record span 31–39 years. Subsequently, given a specific return period, a set of occurrences where volume and peak fall within a bivariate quantile curve is established at a designated watershed. The quantile curves derived from the regional methodology are juxtaposed with those obtained through the local method to assess the efficacy of the MIF technique. The model performed well for Arangali, Kalampur, Pattazhy, Pudur, and Mankara stations, as the quantile curves generated by the regional and local approaches matched well at these watersheds. In contrast, the regional and local quantile curves differ considerably at Perumannu, Ramamangalam, Kidangoor, and Erinjipuzha watersheds, indicating the effect of small sample size, higher sensitivity to local factors, modeling approach, and uncertainty involved. This investigation significantly enhances flood risk assessment in river areas using the MIF method to generate regional quantile curves, identify homogeneous regions, and compare regional and local quantile estimates, improving predictive accuracy at ungauged watersheds. The study confirmed data homogeneity across nine Kerala watersheds, with multivariate discordancy measures ?Di?<2.6, and a homogeneity test H value of -0.76. The BB8 copula best modeled the joint distribution of mean flood volume (V) and peak flow (Q), achieving a Kendall’s tau of 0.711 at Arangali. Regional quantile curves aligned well with standardized data, with the Gaussian copula (?)=0.4427, p<(1.75E-27) selected for multivariate regional analysis. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.
Assessment of Bi-Decadal Groundwater Fluctuations in a Coastal Region Using Innovative Trends and Singular Spectrum Analysis
(Springer, 2023) Krishnan, C.; Mahesha, A.
Coastal areas are among the densely populated regions in the world with growing population and subsequent increasing demands for water. Understanding the long-term variations in available water resources aids in efficient water conservation, management and allocation strategies. The present study investigated the long-term trends in groundwater depths (GWDs) for pre-monsoon and post-monsoon seasons in the coastal district of Kollam during the period 1996∼2017, where groundwater is the primary source for domestic and agricultural uses during summer season. The trends examined using the modified Mann Kendall (mMK), innovative trend analysis (ITA) and Sen’s slope estimator indicated a decreasing pre-monsoon GWD trends at an average of −0.5m/decade in 63% of the wells, while increasing post-monsoon GWD trends at an average rate of +0.43m/decade in 72% of the wells at 5% significance level. The singular spectrum analysis (SSA) captured monotonic as well as non-monotonic trend trajectories for the GWDs. About 41% wells exhibited a correlation below — 0.5 (p<0.05) between post-monsoon GWDs and JJASO (June, July, August, September and October) rainfall totals. The increasing post-monsoon GWDs could be related to recent changes in the southwest monsoon patterns over the peninsular India. Adequate planning and management of existing water resources could impart better control on water conservation strategies under the scenario of climate change. © 2023, Geological Society of India, Bengaluru, India.
Bivariate Drought Characterization of Two Contrasting Climatic Regions in India Using Copula
(American Society of Civil Engineers (ASCE), 2021) Sajeev, A.; Deb Barma, S.; Mahesha, A.; Shiau, J.-T.
This study aims to construct the multiple time-scale joint distributions of drought duration and severity using two-dimensional copulas and compare the drought characteristics in India's two contrasting climate regions: the arid Rajasthan and humid, tropical Kerala. The drought occurrences were defined by the standardized precipitation index (SPI) with a threshold below -0.8 at time scales of 3, 6, 12, and 24 months for 1900-2016. Significant correlations were noted between the drought severity and drought duration in both regions. The Clayton copula gave a better fit than other copulas for modeling the dependence among the observed drought duration and severity. The results indicate that the probability of short-term droughts (SPI-3 and SPI-6) is more significant than those of long-term droughts (SPI-12 and SPI-24) for an identical drought event in both regions. Also, the probability of severe drought events with greater duration and severity for long-term droughts (SPI-12 and SPI-24) is higher in Kerala than that in western Rajasthan. For all the time-scale SPIs, the conditional probability of drought severity for a given duration exceeding a threshold showed an increasing trend in both regions. Furthermore, the conditional probability of the drought duration given the severity for short-term droughts is greater than that of the long-term droughts for the same drought event. For short-term droughts, the conditional return period of an identical drought event is lower in Kerala than in western Rajasthan. In contrast, the conditional return period of long-term droughts is lower in western Rajasthan. Additionally, copula-based nonexceedance conditional distributions for the major crops were established based on rainfall. © 2020 American Society of Civil Engineers.
Characterization of a coastal aquifer—a case study
(2009) Saldanha, J.P.; Vyshali; Mahesha, A.
The present study deals with the characterization of the coastal aquifer in the Pavanje river sub-basin through the pumping tests and the electrical resistivity tests. The electrical resistivity tests are also used to study the geology of the aquifer and possible saltwater intrusion. The transmissivity of the region varies from 50 m2/d—160 m2/d and the specific yield up to 8%. The results of the electrical resistivity tests indicate that the top surface is having a resistivity of 400—1000 ohm-m with an average thickness of 5 m and the bottom layer is having the resistivity of 80—300 ohm-m with an average depth of 20 m. Additionally, an attempt was made to model the groundwater flow in the region using FEFLOW software for the period between June, 2006 and April, 2007. The model is run for different pumping scenarios considering the probable developments in the region. Saltwater intrusion is predicted at some specific locations due to low water table condition during the summer. The water budget estimation indicates that there is significant groundwater outflow from the area during June to November. Hence, it is necessary to carry out investigations to arrest this outflow which may be utilized during the lean season. © 2009 Taylor & Francis Group, LLC.
Characterizing extreme rainfall using Max-Stable Processes under changing climate in India
(Elsevier B.V., 2025) Vinod, D.; Mahesha, A.
Climate change has markedly intensified the frequency and intensity of extreme rainfall events globally over recent decades. The present investigation introduces a novel approach to modeling Intensity-Duration-Frequency (IDF) curves for major river basins in India using max-stable processes (MSPs). In contrast to earlier studies that mainly dealt with univariate extreme value theory and point-based IDF curves, this work uses a variety of MSP characterizations, such as Brown-Resnick, Schlather, Geometric Gaussian, and Extremal-t, to capture the spatial dependencies and non-stationary characteristics of extreme rainfall. This comprehensive two-stage modeling approach incorporates geographical covariates to capture spatial variation in extreme rainfall, followed by additional climate-informed covariates. One hundred fifty-six surface response models are analyzed across nine hourly extreme rainfall durations over 11 river basins. The Brown-Resnick process effectively captured spatiotemporal dependencies across all durations in the annual timeframe, while the Geometric Gaussian process also demonstrated strong performance. During the Indian Monsoon season, distinct covariates such as the Southern Oscillation Index (SOI) and Global Temperature Anomaly (GTA) significantly influenced extreme rainfall patterns. The analysis reveals that the Brahmaputra basin consistently exhibits the highest short-duration extreme rainfall, while the Indus basin shows the lowest. Long-term projections indicate alarming trends, with potential short-duration extreme rainfall reaching 338.9 mm for a 100-year return period in the Godavari basin. The findings highlight the importance of updating IDF relationships in climate variability, providing insights that could lead to disaster preparedness and resilience planning for vulnerable communities across India. © 2025 Elsevier B.V.
Conceptual model for the safe withdrawal of freshwater from coastal aquifers
(2009) Mahesha, A.
The effect of subsurface barrier on the motion of the saltwater-freshwater interface in coastal aquifers is analyzed for wide ranging freshwater pumping scenarios. A Galerkin finite-element model considering sharp interface approach is used for this purpose. A semi-pervious subsurface barrier extending up to impervious bottom of the aquifer is considered at certain distance inland, parallel to the seacoast. The effect of barrier is analyzed in checking the advancement of the saltwater-freshwater interface under different scenarios of freshwater withdrawals at seaward and landward locations of the barrier and compared with nonbarrier conditions. The results indicated that barrier is able to check the advancement of the intrusion significantly and in certain cases, the progress is completely stalled for withdrawals on the landward side. Also, marked variations in the interface profile are observed as compared to no barrier condition, especially, for the seaward freshwater developments. From the model, nearest possible locations from the seacoast have been worked out for the safe withdrawal of freshwater where their effects are negligible on the saltwater advancement. © 2009 ASCE.
Conjunctive use in India's Varada River Basin
(American Water Works Association cs-journals@wiley.com, 2009) Ramesh, H.; Mahesha, A.
The use of groundwater in conjunction with surface water resources has gained prominence in regions experiencing scarce or uneven distribution of water. In the Varada River Basin in Karnataka, India, for example, an optimization model was developed for the conjunctive use of surface water and groundwater resources because of the increasing demand on agricultural and domestic sectors of this area's water supply. Monsoon rains, which occur only six months a year, predominantly control the basin's agricultural activities. However, the area has an immense need for efficient use of available water resources during the rest of the year. The model, based on linear programming, optimizes the allocation of groundwater and surface water subject to hydraulic and stream flow constraints. The model incorporates policy scenarios that add to the sustainability of the system. The developed conjunctive-use model is simple but effective in computing the optimal use of the Varada basin's water resources.
Copula-Based Frequency and Coincidence Risk Analysis of Floods in Tropical-Seasonal Rivers
(American Society of Civil Engineers (ASCE), 2021) Muthuvel, D.; Mahesha, A.
The conventional method of univariate flood frequency analysis based solely on peak flow (Q) overlooks the influence of other characteristic flood variables, such as the accumulated volume (V) of the flood and the duration (D) of flood events. A copula-based multivariate model that represents the joint behavior of these dependent flood variables could aid in computing joint return periods of flood events in tropical, seasonal rivers of India. In connection with the potential locations of high flood risk among west-flowing rivers, multivariate flood frequency analysis was performed on the Bharatapuzha, Periyar, and Chaliyar Rivers of the state of Kerala, India. A comparison of univariate return periods with multivariate return periods reveals that the intersection of flood variables corresponding to a 20-year univariate return period yields a trivariate return period of 91 years at Bharatapuzha and 144 years at Periyar and Chaliyar. The return period by the union of such flood variables is 10 years. The choice of flood variables and their combination depend on the problem at hand. Additionally, basinwise confluence flood frequency models are built with the peak flow at each stream as the random variables show their spatial interdependencies using conditional probabilities and return periods. The copula-based flood coincidence risk model captures the temporal aspect of the co-occurrence of flood peaks in a basin's streams. The co-occurrence of annual flood peaks between the stream pairs of the Bharatapuzha, Periyar, and Muvathapuzha basins is the highest toward the end of July with probabilities of approximately 2.2×10-4 (at the Kumbidi and Mankara stations), 3×10-4, and 1×10-3, respectively. A trio of copula-based multivariate flood frequency, confluence flood frequency, and flood coincidence risk models could be used to design safe and economic hydrologic infrastructure. © 2021 American Society of Civil Engineers.
Decadal trends and climatic influences on flash droughts and flash floods in Indian cities
(Elsevier B.V., 2024) Archana, T.R.; Vinod, D.; Mahesha, A.
Flash droughts/floods are extreme weather phenomena that are expected to become increasingly frequent and severe with the changing climate. Flash droughts result from a rapid decline in soil moisture, while flash floods occur due to a high extreme rainfall intensity over a short duration. This study analyzes the ERA5 reanalysis data (hourly temperature, soil moisture, and precipitation) from 1992 to 2022 to assess flash drought/flood attribute variations across fourteen Indian cities. Flash drought events are identified based on specific conditions using the obtained Soil Moisture Index (SMI) values. At the same time, we propose a novel approach to attribute flash floods by setting thresholds for precipitation and soil moisture. This study examines the frequency and trends of flash drought and flood events across India's various Köppen-Geiger climatic zones from 1992 to 2022. Jaipur and Dehradun show a statistically significant decrease in flash drought events with magnitudes of ?0.0833 events/year and ?0.0769 events/year, respectively. Conversely, Hyderabad exhibits a highly significant increase in flash flood events with a magnitude of 1.1851 events/year. Similarly, Bengaluru, Varanasi, and Vishakhapatnam also show substantial increases in flash flood events. These findings underscore the impact of climate change on flash droughts/floods, highlighting the necessity for sustainable strategies. © 2024 Elsevier B.V.
Development of operation policy for dry season reservoirs in tropical partially gauged river basins
(Taylor and Francis Ltd., 2024) Gowda, C.C.; Mahesha, A.; Mayya, S.G.
The present investigations focus on developing an appropriate model for streamflow generation of a partially gauged basin and the operation of small storage in a tropical, seasonal river basin. The small storages created through the vented dams effectively conserve and sustain the water resources for the lean season. Hence, it is pertinent to develop streamflow models to derive streamflow series at the vented dam locations. In the present investigation, streamflow modelling was attempted using response surface and neural network models in a first of its kind. Out of them, the Response Surface Box Behnken model was found to be most efficient in generating streamflow, with Nash Sutcliff's efficiency above 0.617. Further, it is also essential to operate these small storages to maintain a sustainable, ecological flow in the river course. The operation policy for seasonal storage like vented dams is yet to be reported in the literature. The present work uses reservoir simulation and multi-objective optimisation to derive such storages operation policy through hedging operations, with modified shortage index and mean event deficit as objectives. The performance indicators evaluated the operation policy for eight vented dams of the basin. The results illustrate that vented dams indicate shortages while satisfying the respective demands. The results demonstrate that hedging improves the reservoir's performance by reducing the mean event deficit of 0.268–0.044 Mm3 before and after hedging. The frequency and intensity of shortages were also reduced through hedging for the tropical, seasonal river basins. © 2022 International Association for Hydro-Environment Engineering and Research.
Downscaling algorithms for CMIP6 GCM daily rainfall over India
(Springer, 2024) Raj, R.; Vinod, D.; Mahesha, A.
The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R2 = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data. © Indian Academy of Sciences 2024.
Effect of climate change on Netravathi riverflow
(2010) Shetkar, R.V.; Mahesha, A.
The adequacy of freshwater resources for future is difficult to assess due to complex and rapidly changing environmental and social parameters. There is uncertainty with respect to the prediction of climate change and its effect on planning and management of water resources. Higher temperature and reduced precipitation would lead to larger deficiencies in the supply and demand for water. This might cause deterioration in the quality of freshwater adding strain on the already fragile balance between supply and demand. Although the effect of climate change on water resources is uncertain and site specific, the perception is that it will result into increased extreme events and hence increased risk of flooding and droughts. This paper aims at assessing the trends of temperature, precipitation and river flow for the Netravathi river, a tropical river of south India. The river water utilization at present is less than 1% of the average annual flow. The river flow is neither controlled nor altered due to manmade structures hence may be considered as natural flow. From the analysis, it is important to note that the temperature is rising and there is declining trend in precipitation and stream flow during the study period of 30 years (1971 to 2001). Also, the low flow frequency analysis shows an upward trend. Similar analyses carried out for the number of days of flow peaks above a threshold value indicate that the high flow frequency trend is declining and the magnitude of these high flow events is also decreasing. The outcome of the present study indicates a definitive, decreasing trend in the river flow due to climate change and a forecasting mechanism may be essential in the future for the sustainable development of the available water resources. © 2010 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.
Effect of permeability of subsurface barrier on salt water intrusion in coastal aquifers
(2006) Mahesha, A.; Lakshmikant, K.
Semi-pervious, subsurface barriers are considered to be one of the viable solution for the control of saltwater intrusion in coastal aquifers. Investigations are carried out in this work to assess the performance of the barrier with different permeabilities using a finite element model. The advancement of saltwater - freshwater interface and the water table profiles are monitored for fresh water withdrawals at inland locations under the scenarios of wide ranging hydraulic conductivities of the barrier. The results indicated that barrier is able to check the advancement of the intrusion significantly with the hydraulic conductivity being less than certain limit. The performance of the barrier is also compared with no barrier conditions. Marked variations in the interface and water table profiles are observed as compared to no barrier condition. The performance of the barrier was not found to be satisfactory beyond certain limit of permeability.
Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India
(Springer Science and Business Media Deutschland GmbH, 2021) Sharannya, T.M.; Venkatesh, K.; Mudbhatkal, A.; Muthuvel, M.; Mahesha, A.
This paper assesses the long-term combined effects of land use (LU) and climate change on river hydrology and water scarcity of two rivers of the Western Ghats of India. The historical LU changes were studied for four decades (1988–2016) using the maximum likelihood algorithm and the long-term LU (2016–2075) was estimated using the Dyna-CLUE prediction model. Five General Circulation Models (GCMs) were utilized to assess the effects of climate change (CC) and the Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the two river catchments. To characterize granular effects of LU and CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006–2040), mid-future (2041–2070), and far-future (2071–2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the present-day LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
EVALUATION OF ERA5 AND IMERG PRECIPITATION DATA FOR RISK ASSESSMENT OF WATER CYCLE VARIABLES OF A LARGE RIVER BASIN IN SOUTH ASIA USING SATELLITE DATA AND ARCHIMEDEAN COPULAS
(Zibeline International Publishing Sdn. Bhd., 2022) Deb Barma, S.D.; Uttarwar, S.B.; Barane, P.; Bhat, N.; Mahesha, A.
Precipitation as a major water cycle variable influences the occurrences and distribution of terrestrial water storage change (TWSC), evapotranspiration (ET), and river discharge (Q) of a large river basin. However, its relationship with the other water cycle variables using probabilistic dependence structure concept has not been addressed much. Furthermore, precipitation derived from gauge record is plagued by bias due to orography and under-catch. To fill these gaps, bivariate copula and precipitation derived from reanalysis and satellite data were used. In the present study, the basin-wide averages of the precipitation products APHRODITE, ERA5, and IMERG were used as predictors, whereas the areal mean of MOD16 evapotranspiration, GRACE TWSC, and gauge discharge were used as dependent variables (predictants) for the Brahmaputra basin. The bivariate Archimedean copulas were applied to all the pairs of precipitation-TWSC, precipitation-ET and precipitation-Q based on the optimal marginal distributions obtained. Using the best copula for each pair of the variables, the conditional probability was constructed to predict the predictants for different precipitation amounts (5th, 25th, 50th, 75th, and 95th percentiles). The focus of the analysis was on two scenarios of the predictants (i.e.,≤ 5th and ≥ 95th percentiles). The non-exceedance conditional distribution of TWSC, ET, and Q (all predictants ≤ 5th percentile) decreases with precipitation increase. However, the exceedance probability of the predictants (≥ 95th percentile) increases gradually with an increase in precipitation. The results revealed that both ERA5 and IMERG precipitation data could be used to derive probabilistic measures of the water cycle variables in the absence of gauge-based precipitation. © 2022 by the authors.
Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain
(Taylor and Francis Ltd., 2024) Gautam, J.; O, S.; Vinod, D.; Mahesha, A.
Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrological models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall–runoff modelling across Great Britain. Three IMERG V06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the simple water balance model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that European Observation (E-OBS) ground-based data yield the highest Nash-Sutcliffe efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG’s utility in hydrological modelling for ungauged or poorly gauged basins. © 2024 IAHS.
Fortnightly Standardized Precipitation Index trend analysis for drought characterization in India
(Springer, 2024) Benny, B.; Vinod, D.; Mahesha, A.
Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
Future global concurrent droughts and their effects on maize yield
(Elsevier B.V., 2023) Muthuvel, D.; Sivakumar, B.; Mahesha, A.
Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950–2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950–2014), near future (2021–2060), and far future (2061–2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions. © 2022 Elsevier B.V.
Geomorphology and hydrogeology of coastal tracts of the Central West Coast of India
(2013) Honnanagoudar, S.S.; Venkat Reddy, D.; Mahesha, A.
Dakshina Kannada district which is a coastal district of Karnataka, spreads along the west coast of India covering coastal tract of about 40 km. Dakshina Kannada district is divided into three regions, low land, mid land and high land. The coast line is generally straight and followed the Dharwar rocks trending NNW-SSE.The rocks weathered fractured and jointed granitic gneisses and laterite varies from 10 m to 30 m. The thickness of coastal alluvium varies from 7 to 29 m. There are spits and beach ridges available in coastal tract. The Netravati river flow towards Mangalore where it joins Gurupur River and both the Rivers from common esture discharging into the sea. Gneissic rocks archaean age cover a major part of this region as basement rocks. These are elsewhere overlain by oligomictic quartz conglomerate belonging to Dharwar super group. The recent alluvium and colluvial deposits occur along the river bed and sea coast. The exposure of crystalline rock found as isolated hills along the shore and off shore. The black clay marine sediments with a thickness of 0.30 m to > 1m occur as lenses along the coast and in the deltaic islands. Its occurrence as marked at a depth ranges of 5 to 6 mbgl. The groundwater below the black clay horizons of coastal sediments found with high salinity, which marks the index bed for saline water and fresh water interface. © 2013 CAFET-INNOVA TECHNICAL SOCIETY.
Groundwater level modeling using Augmented Artificial Ecosystem Optimization
(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.
Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.