Journal of Water Engineering and Management
From Water Bankruptcy to Climate-Resilient Sustainability: A Decentralized Water Management Roadmap for Tamil Nadu Water Self Sufficiency
Tamil Nadu is facing an ever-growing water crisis because of unpredictable monsoons, urbanization, and poor water management. By 2050, demand is expected to outstrip supply. A lot of rain falls, but a lot of it is wasted because of bad storage, encroachments, and water bodies that aren't used enough. The state has a lot of wells, lakes, and tanks, but many of them don't work or aren't useful. The proposed water self-sufficiency project aims to fill all traditional tanks and lakes with flood water surplus using automation and modern technologies, in addition to maximizing rainwater harvesting, restoring and connecting water bodies, and increasing storage capacity through modern technologies like GIS and remote sensing. This means that all Ayacut laws need to be changed. It's very important to retrofit existing TWAD water supply systems so that they can send extra floodwater into tanks and ponds to recharge groundwater. A big part of the plan is to turn non-system tanks into system tanks and make sure that water is distributed in a decentralized way. The initiative also puts a lot of stress on using a lot of drip irrigation in areas that get rain to boost agricultural productivity while saving water. Another goal of treating wastewater and using it again in industry is to cut down on the need for fresh water. Even though interstate water projects are being thought about, the main goal is still to make the best use of internal resources.
Assessment of Hydrological Intervention for Hallur (4d4d4b) Sub Watershed of Rattihalli Taluk, Haveri District, Karnataka
The Halluru sub-watershed in Rattihalli taluk, Haveri district, demonstrates complex hydrological and agro-ecological dynamics shaped by topography, land use, soil properties and climatic variability. DEM analysis reveals elevations ranging from 450 to 808 m. Actual evapotranspiration (AET) exhibits considerable spatial and seasonal variation, from 542 to 988 mm, with lower values in high-elevation rainfed areas and higher values in low-lying irrigated zones with deeper soils and denser vegetation. Land use was predominantly agricultural (≈50.7% cropland; 6.1% plantations), with minimal forest cover (0.2%) and moderate wasteland (3.3%), water bodies, and built-up areas (4.6%). Surface soils vary widely, with clayey soils dominating central and southern regions (infiltration ~2 mm/hr), clay loam in scattered patches (~7 mm/hr) and sandy clay/loamy soils in uplands (~10–14 mm/hr), influencing runoff. Rainfall analysis (2014–2022) showed that average annual rainfall of 826 mm, with kharif rainfall (≈60%) deficient in 2015, 2016 and 2018, rabi rainfall (≈20.29%) severely deficient in 2016 and 2017 and summer rainfall (≈19.37%) contributing to pre-monsoon soil moisture. The annual number of rainy days ranged from 12–25, with 0-9 days producing runoff (20-30 mm), and >30 mm events recorded only in 2021–2022. Satellitederived soil moisture maps indicate high heterogeneity, with Kharif values up to 0.28 m3 /m3 in valleys and Rabi values declining to 0.10–0.16 m³/m³ in uplands, highlighting seasonal moisture stress. Groundwater levels (2014– 2023) showed seasonal fluctuations with a long-term declining trend, exacerbated during 2019–2020 by low rainfall and intensive pumping.
Rainfall-runoff Modelling of Amochu Basin in Bhutan using HEC-HMS Model with Remote Sensing and GIS-based Parameterization
Modelling surface runoff of a basin is important for integrated water resource management, planning developmental activities, flood mapping and zoning, safeguarding human lives and building resilient flood protection structures downstream. The low-lying plain of Amochu basin is no exception to flood and natural disaster. Recurrent floods have led to loss of life and property. HEC-GeoHMS and HEC-HMS models were applied to generate and simulate river runoff from daily precipitation fusing in with other basin parameters. The input parameters for rainfall-runoff modelling were carried out effectively by using GIS and Remote Sensing techniques. application of Remote. The simulation analysis of rainfall-runoff model combines soil conservation service-curve number (SCS-CN) for loss calculation, SCS Unit Hydrograph for transformation and Muskingum methods were implemented for routing of flow. The streamflow stimulated using HEC-HMS model were analysed and compared with the existing observed streamflow data. The model efficacy was evaluated using Nash-Sutcliffe Efficiency Coefficient (NSE) and Root Mean Square Standard Deviation (RMSE). For the annual time series (January-December), the average values of NSE were 0.68 and 0.64 for calibration and verification, correspondingly. For the seasonal time series (June-September), the model achieved average NSE value of 0.66 and 0.65 during calibration and verification, respectively. The association of streamflow from model simulation verified satisfactory agreement with the detected streamflow values during both calibration (R2=0.6) and validation (R2=0.6). The average area weighted Curve Number (CN) derived from 11 sub-basins of Amochu was 75.7
Reduction of Lead Bioavailability and Phytotoxicity in Pb-Slag Contaminated Soil Using Soil Mixing and Compost-Modified Biochar
Food security and human safety are being threatened by heavy metal contamination of agricultural lands. Several physical and biological methods have been employed separately for soil remediation. In this study, effects of combining soil mixing with compost (C) and cow-dung (CD) for reduction and immobilization of Pb in the soil as well as growth, Pb uptake and biochemical responses of maize grown on contaminated soil were investigated. Lead contaminated soil mixed with uncontaminated soil at different ratios to give 0%, 25%, 50%, 75% and 100% levels of contamination were further mixed with C and CD at four different levels (0, 10 20 and 30 t/ha) before planting of maize. Data were collected on maize growth and yield, Photosynthetic Pigments (PP), stress indicators (proline and glycine betaine) contents as well as Pb uptake by maize crop and post-cropping soil Pb concentrations. Pb concentration was reduced by 29-85% in soil mixtures while, C and CD further reduced Pb by 69-77% and 42-66%, respectively. Combined treatments reduced Pb uptake, enhanced maize tolerance and growth as no germination was recorded in 100% Pb contaminated soil. Dry matter accumulation and PP were more in maize crop grown on compost treated soils compared with those grown on soil mixtures only. Proline and glycine betaine contents in maize leaf were reduced in treated plants than untreated plants. Combination of soil mixing with organic amendments could be a better approach for reducing Pb uptake by maize crop and enhanced maize growth on contaminated soil.
Phytoremediation Potential of Leucaena Leucocephala on Heavy Metal and Crude Oil Polluted Soils Amended with Biochar and Compost
Soil contamination from heavy metals and crude oil is a global concern globally with the challenge being felt most in developing countries where remediation options are limited. In this study, the phytoremediation potential of Leucaena leucocephala and the ameliorative effects of biochar and compost on plant growth in contaminated soils were investigated. Lead-contaminated soil collected from a battery waste dumpsite was mixed with normal soil to get 50% contamination level, Soil artificially spiked with crude oil at 5% w/w was used as crude oil polluted soil. Both soil types were treated with compost and biochar at four application rates (0, 2.5, 5, and 10 t/ha). The compost and biochar were applied singly or combined depending on the treatment. Leucaena leucocephala seeds were planted one week after application. Data were collected on plant height, number of leaves, biomass, secondary metabolites (proline and phenolics) and lead accumulation in plant tissues. Lead contamination caused stunted plant growth in unamended soil, while addition of organic amendments enhanced plant’s performance despite different levels of soil contamination. On the osmolyte production, plants in untreated lead-contaminated soil produced more proline and phenolic compounds than those grown on amended soil. The translocation factor for Pb exceeded 1.0, in most treatment combinations, but the biological absorption factor was relatively low. In conclusion, Leucaena leucocephala could be considered as a credible candidate for phytoremediation of lead contaminated soil particularly in the presence of organic soil amendments.
Analysis and Monitoring of Groundwater Vulnerability and Quality in the Sudano-Sahelian Zone: Case of the Yagoua Rice-Growing Basin, Cameroon
The main aim of this study is to determine whether the groundwater in the Yagoua rice-growing basin is of satisfactory quality for human consumption. A pollution vulnerability map of the study area was first produced using the modified DRASTIC method. The area was then subdivided according to the vulnerability map results to guide sampling, and finally, the groundwater of the basin was classified. Intrinsic vulnerability mapping showed that the study area is dominated by very low to low vulnerability classes, covering 97% of the total area. The basin was divided into three zones based on vulnerability: zone 1 corresponds to the very low vulnerability class, zone 2 to the low vulnerability class, and zone 3 to the medium vulnerability class. Physicochemical analysis results indicated that iron is the major element, with concentrations well above the maximum allowable limit. The Piper diagram showed that 60% of the analyzed water has a calcium-magnesium bicarbonate facies. The Water Quality Index (WQI) reveals that 53% of the study area has non-potable groundwater, linked to high iron content. Groundwater in the Yagoua rice-growing basin is unfit for human consumption and requires prior treatment. Modelling shows that, higher permeability and deeper wells or water table generally have older groundwater and less vulnerability to pollution.
Smart Water Resource Management using IoT and Data Analytics
Water is essential for life and as well as for our economic growth as a precious resource. Water scarcity and inefficient management are still major challenges for economic and environmental sustainability to the rest of the world. Traditional water management ICT systems are often proprietary and lack interoperability, which limits effective manual monitoring, control, and fault detection and real-time data collection, that leads to immense water wastage. This research work proposes a smart water management mode that uses IoT to decouple decision support and monitoring from underlying business processes and vendor- specific subsystems. The model enables heterogeneous devices to work together in a unified, interoperable way. The study highlights how IoT based smart water systems can improve data accuracy, resource allocation, leak detection, and overall sustainability and resilience of water infrastructure.
Machine Learning-Based Streamflow Prediction for Hydrological Applications: A Case Study with LSTM and Random Forest
Flood forecasting plays a vital role in disaster management within water resources engineering. This study evaluates the effectiveness of two data-driven approaches: a Long Short-Term Memory (LSTM) recurrent neural network and a Random Forest (RF) regression model for predicting river discharge using a publicly available hydrological dataset. Historical streamflow data, including lagged flow and precipitation variables, serve as inputs to the models. Performance metrics such as root-mean-square error (RMSE), mean absolute error (MAE), Nash– Sutcliffe efficiency (NSE), and coefficient of determination (R²) are employed for model evaluation. Results indicate that the LSTM model exhibits superior predictive performance, with lower RMSE and MAE and higher NSE and R² values. These findings support the advantage of recurrent neural networks in modelling temporal hydrological patterns.
Application of Multi-Criteria Decision Analysis Technique for Flood Susceptibility Mapping in an Urban Watershed of Bagmati Basin, Nepal
Bagmati Basin within Kathmandu Valley possess the challenge of reoccurring floods mainly in the urban and peri-urban areas. Hanumante watershed, a northeastern tributary of the Bagmati basin in the Kathmandu Valley, is a typical case for urban/peri-urban flooding. Significant recent flood events in the Hanumante Watershed occurred in 2015, 2018, and 2023. Flood assessments in Nepal have traditionally relied on hydraulic models combined with statistical extrapolation of flood discharge. Whereas, this research evaluates the use and utilization of Multi-Criteria Decision Analysis (MCDA) for GIS-based flood mapping. The framework of this study is both innovative and robust, utilizing a unique two-tier MCDA process, with results validated against two different sets of ground-level data. Flood hazard susceptibility was mapped using the Analytical Hierarchy Process (AHP) method, considering 15 parameters categorized under 4 (four) criteria namely Hydrometeorological, Morphological, Soil-permeability, and Land use-land cover (LULC) dynamics. The flood susceptibility map, was validated with the 1-D analysis of Hydrologic Engineering Centre’s River Analysis System (HECRAS) model, a flood benchmarking survey data of the area, achieving an Area Under the Receiver Operating Curve (AUC) >90. Furthermore, the map was verified with georeferenced ground level photographs of Hanumante Flood 2023, demonstrating the accuracy and applicability of the proposed framework of this elaborated study.
Evaluation of Temperature and Precipitation Changes under Climate Change Scenarios in Budhi Gandaki River Basin (BGRB), Nepal
Climate change has posed a solemn threat to the hydrological firmness of mountainous river basins, with broad impressions on ecological systems, water resources, and hydropower development. This study evaluates the future effects of climate change over the Budhi Gandaki River Basin (BGRB) in Nepal using a collaboration of CMIP6 General Circulation Models (GCMs) under Shared Socioeconomic Pathways 2-4.5 and 5-8.5. Quantile mapping, as an advanced bias correction technique, was applied to enhance the models' accuracy in reproducing the past climate pattern, thereby improving the robustness of the future projection. Conferring to the verdicts, significant warming with maximum temperature increases by the end of the 21st century can be expected over a range of 1.18 to 5.08°C. Some other key seasonal changes noticed in precipitation regimes include increased seasonality, increased winter rainfall, and variable monsoon activity. The consequences highlight the urgent need to integrate bias-corrected climate projections into regional progress and climate adaptation plans. It calls for community-based adaptation approaches coupled with strong infrastructure and sustainable water management regulations so that the emerging risks could be reduced. Our work delivers essential new knowledge linked to the regional effects of climate change in high-altitude basins, confirming the urgent necessity for prompt, evidence-based decision-making to foster climate resilience and sustainable development in Nepal's Himalayan region.
Hydraulic Analysis of Irrigation Canal Using HEC-RAS Model: A Case Study of Latifshah Weir in Uttar Pradesh, India
India's expanding population raises the need for water, notably in agriculture, which consumes around 70-80% of the country's water. As competition for water resources intensifies across different sectors, increasing the efficiency of irrigation canal systems that provide water to agricultural crops is important. Improving these systems can assist in saving limited water resources, expanding irrigated areas, and enhancing local stakeholders' socio-economic conditions. In the present study, the HEC-RAS model was applied to analyse the hydraulics of the left Karmanasa canal irrigation system in Uttar Pradesh, India. Hydraulic models are important tools for studying the flow dynamics of open channels. The HEC-RAS model was calibrated and validated to simulate the water depths. The model was tested across a range of Manning's n values from 0.020 to 0.025. The calibrated value was determined to be 0.023. Calibration and validation of the model produced good results at all sites, with Coefficient of Determination (R2 ) and Correlation Coefficient (r) values ranging from 0.99 to 0.80 and the value of Nash-Sutcliffe efficiency (NSE) ranged from 0.90 to 0.60. The results can be used for effective water management in canal irrigation systems by reducing losses and operation and maintenance costs.
A Smart IoT Architecture for Continuous Health Monitoring Using Raspberry Pi for Pandemic and Emergency Conditions
The increasing demand for noncontact and continuous health monitoring during pandemic and emergency conditions has significantly accelerated the adoption of Internet of Things (IoT) technologies in healthcare. IoT health monitoring systems obviate the need for frequent doctor visits and patient-physician consultations. This paper presents an IoT-based real-time patient health supervision system using Raspberry Pi for track a person's temperature, pulse rate, humidity, and oxygen saturation. This approach is particularly beneficial in rural or village areas, where local clinics can share their patients’ medical information with larger city hospitals for better diagnosis and treatment. The proposed system integrates temperature, pulse rate, and humidity sensors to observe the patient’s health status with high accuracy. The suggested system consists of three parts: an inexpensive, lightweight IoT sensor, a mobile cloud server called Thing Speak, and a qr code scanning technique for data processing and diagnostics. ThingSpeak updates the data, and then the patient's health status is displayed. Although motivated by COVID-19 needs, the system is designed as a general-purpose telehealth solution suitable for elderly care, chronic disease monitoring, home quarantine, remote hospitals, and future pandemic preparedness. Experimental results demonstrate that the system is cost-effective, scalable, and reliable for modern healthcare environments.
Design of Water Distribution Network for Malliala Village, Telangana using EPANET
Water is one of the most essential natural resources supporting life on Earth, and both its availability and quality play a critical role in shaping human well-being and livelihoods. Although freshwater exists in abundance globally, it is often unevenly distributed in space and time, and its quality may not always be suitable for domestic use. In this context, the present study focuses on developing an efficient water distribution network for Malliala Village using the EPANET platform to reliably meet domestic water requirements. As population growth directly influences future water demand, population projection techniques were applied to estimate the design-period population and corresponding water needs. Spatial information required for network planning was generated using Google Earth Pro and ArcGIS, enabling accurate assessment of village layout, settlement patterns, and service coverage. The design approach incorporated the number of households, projected population, estimated demand, and discharge from the overhead tank. The network consists of concrete and HDPE pipes, assigned roughness coefficients of 140 and 145 respectively, and was formulated for continuous 24-hour operation. Hydraulic analysis was carried out using EPANET 2.0 to determine node-specific parameters such as actual demand, pressure, and hydraulic head, along with pipe characteristics including flow, velocity, and unit head loss. The results confirm that the designed system is capable of delivering adequate water throughout the network under the assumed operating conditions.
Estimation of Crop Evapotranspiration for Rabi crops using Spatial and Ground based Observation: A Case Study of village Akodha, Prayagraj
The primary objective of this research is to estimate crop evapotranspiration (ETc) by integrating spatially distributed remote sensing observations with ground-based meteorological data. Grass reference evapotranspiration (ETo) and CROPWAT based evapotranspiration were simulated using satellitederived ET inputs generated through the Surface Energy Balance System (SEBS) model, along with field-measured meteorological parameters collected from six sampling points during the Rabi season. The SEBS-derived ET values were found to be realistic and successfully captured the seasonal variability across the study area. Using Grass-based ETo, the maximum ETo was observed in March (5.73 mm/ day) corresponding to the highest temperature, while the minimum ETo occurred in December (0.47 mm/day) during the lowest temperature period. Similarly, CROPWAT-derived ETo also peaked in March (4.06 mm/day) and reached its minimum in December (1.04 mm/day), consistent with seasonal temperature variations. These results highlight the potential of satellite-based ET estimation in improving spatial and temporal understanding of crop water requirements during the Rabi season.
Developing Water Management Strategies using Water Consumption Uniformity, Relative Evapotranspiration and Crop Water Productivity
Accurate and reliable information on irrigation practices and crop water use is essential for evaluating and improving existing water management strategies. This study assesses irrigation equity, adequacy, and crop water productivity for two water-intensive crops—Jute and Rice—cultivated across West Bengal from 2012 to 2022. Actual crop evapotranspiration (ETa) was derived using the Google Earth Engine Evapotranspiration Flux product. Irrigation performance indicators were computed using Relative Evapotranspiration (RET) for equity and the Coefficient of Variation (CV) for adequacy. Crop type classification was carried out using the Random Forest Algorithm, a supervised machine learning technique. Crop water productivity (CWP) was estimated as the ratio of crop yield to crop water use, with crop yield variability inferred from Normalized Difference Vegetation Index (NDVI) patterns. For jute and rice, the relative errors in the evapotranspiration estimates ranged from 7–27% and 0–3%, respectively. Spatial fluctuation in ETa varied between 10% and 35%, reflecting substantial heterogeneity in water use across the study region. Higher values of RET indicate optimal irrigation, while lower values signify water stress. The analysis revealed that approximately 32.5% of the jute-growing area and 13% of the rice-growing area experienced excess water application, whereas 41.73% of jute and 37.30% of the rice areas were waterdeficient. These findings indicate significant spatial non-uniformity in irrigation practices, which may contribute to localized water scarcity and reduced water-use efficiency if not addressed through improved water management interventions.
Rainfall Trend and Drought Analysis in Magadh Region Bihar
Climate change is increasingly influencing regional hydrology, and Bihar reflects this sensitivity through contrasting climatic patterns. While northern Bihar regularly experiences floods, the southern part, including the Magadh division, is witnessing declining rainfall and recurring droughts. This study examines longterm rainfall trends in the five districts of Magadh division—Arwal, Aurangabad, Gaya, Jehanabad, and Nawada, using rainfall records from 1970 to 2020. The Mann–Kendall test and Sen’s slope estimator were applied to analyze annual, seasonal, and monthly precipitation trends. The results indicate a significant decrease in rainfall in Nawada, Aurangabad, and Jehanabad. Nawada, in particular, shows a notable reduction in both monsoon (4.8 mm/year) and annual rainfall (5.8 mm/year), suggesting increasing hydro-climatic stress. Frequent drought events, occurring every 2-4 years, were also observed across the region. To assess drought severity, the standardized precipitation index (SPI) was computed by fitting the long-term rainfall data to a probability distribution and normalizing it. Positive SPI values indicate wetter-than-normal years, while negative values represent rainfall deficits. The SPI results reveal extreme drought occurrences in Nawada, Aurangabad, Gaya, and Jehanabad, with severe drought recorded in all districts. Nawada experienced the highest drought frequency (four severe events), followed by Aurangabad and Gaya (three each), Arwal (two), and Jehanabad (one). These findings underscore the growing vulnerability of southern Bihar and highlight the need for climate-resilient planning and drought-mitigation strategies.
Arsenic Contamination in the Groundwater of India and Bangladesh and Green Practices in Concrete Construction incorporating Arsenic-Laden Sludge as Admixture
Geogenic arsenic contamination in groundwater represents a critical public health emergency across South Asia. This study presents a comprehensive regional assessment of arsenic distribution patterns across ten Indian states and Bangladesh, synthesizing state-level contamination data with health impact trajectories and remediation technology performance. Exposure affects an estimated 22.38 million people across ~1,800 habitations in India alone. We examine both biogeochemical mechanisms driving arsenic mobilization in alluvial and hard-rock aquifers and epidemiological evidence for multi-system health effects. Critical examination of existing remediation approaches reveals significant gaps in acceptability, sustainability, and cost-effectiveness. This paper proposes an innovative circular-economy solution: stabilization of arsenic-laden sludge from treatment plants within non-structural concrete elements—a green practice that addresses the "end-of-pipe" waste challenge while adhering to environmental safety thresholds. Recommendations include staged implementation protocols prioritizing government infrastructure, rigorous long-term monitoring of leachate pathways, and comprehensive risk communication frameworks for affected communities.
Fluoride Contamination in Groundwater in Northern India
Fluoride's impact on human health varies based on its concentration in drinking water, being potentially beneficial or harmful. As specified by WHO, the suggesting maximum limitation of fluoride in potable water is approximately 1.2– 1.5 mg/L. To better understand fluoride toxicity, this paper examines the adverse causes of excessive fluoride on various biological systems. Elevated fluoride levels in potable water can lead to several health issues, including dental fluorosis, skeletal fluorosis, and long-term effect to the kidneys, liver, and brain. This paper also reviews various techniques for eliminating fluoride from potable water. High concentrations fluoride are often associated with sodium bicarbonate-type water that is deficient in calcium.
Estimation of Evapotranspiration using Spatial and Ground Based Observations for Irrigated Areas of Allahabad
A comprehensive study to determine the evapotranspiration for irrigated areas of Allahabad, India using Quantum GIS, Grass GIS, Python programming of SEBAL and Cropwat 8 is made for the period of January to May for 4 years (2015-2018). Landsat 8 data, DEM, weather data like rainfall, temperature (min. & max.), relative humidity, solar radiation, wind speed, and observed PET data which are used for the SEBS model. DEM was downloaded from SRTM and Landsat 8 data taken from USGS. The rainfall and temperature data which is collected from department of Forestry and Environment SHUATS Allahabad. The entire study compares ET estimated from SEBS and Penman Monteith single source approach model. SEBS approach for the estimation of ET and the Penman Monteith single source approach method (FAO 56) concludes the average minimum and maximum value of ET is 1.07 to 5.40. and 1.02 to 3.17. The Spatial and ground based observations of the study area during the day concludes the co relation of SEBS ET and Penman Monteith single source approach method (FAO 56) ET have coefficient of determination R2 = 0.845. Technique were developed for the continuing determination of monthly evapotranspiration from the irrigated areas.
Bihar's Battle Against Flood: Unravelling the Science of Flood Forecasting and the Primacy of Multi-Source Data Integration
The state of Bihar in India is often vulnerable to catastrophic floods during the monsoon season due to its vast alluvial plains and the systematic pattern of transboundary Himalayan Rivers Systems that flow through it, with their origins in the Himalayas. These floods result in the loss of lives, livelihoods, and infrastructure, among other effects, leading to socio-economic losses that affect millions of people. As such, efficient flood prediction and early warning mechanisms are not just a technical wish; there is an urgent need to incorporate flood prediction and early warning systems into disaster risk reduction and climate change adaptation in the region. This article provides a detailed examination of the flood forecasting paradigm in Bihar, particularly its evolution over the years, from traditional empirical forecasting techniques to more advanced hydrological and hydrodynamic models based on data. It cuts across the institutional structure headed by agencies such as the Central Water Commission (CWC) and the Bihar State Disaster Management Authority (BSDMA), which are responsible for making predictions and distributing them. One of the main points of this study is the importance of data in present-day forecasting, which is the lifeblood of the modern era. We examine the complex procedure of incorporating multi-source information streams, including ground-based data, satellite-derived rainfall information, real-time river gauge and discharge data, high-resolution topographical data, and essential transboundary hydrological data provided by Nepal.
Frequency Analysis of Rainfall Data for Roorkee, Uttarakhand, India
Daily rainfall data for 20 years (2005–2024) were obtained from the Global Weather Data portal for SWAT modelling applications. The dataset was processed to determine the annual maximum and minimum daily rainfall values, as well as to examine monthly rainfall variability. In addition, the standard deviation and coefficient of variation (CV) were computed to evaluate the consistency and variability of rainfall during the study period. The analysis revealed that the maximum daily rainfall during the 20-year period ranged from 18.8 mm to 632.5 mm, indicating a substantial inter-annual fluctuation in extreme rainfall events. Monthly rainfall assessment showed that October consistently received the lowest rainfall among the monsoon-related months, whereas July and August recorded the highest rainfall across all years. The lowest coefficient of variation was observed in July (50.3%), followed by September (53.6%), signifying relatively stable rainfall during peak monsoon months. Conversely, the highest CV was recorded in June (77.4%), reflecting significant variability in early-monsoon rainfall. Frequency analysis was carried out using Normal, Log-Normal, and Gumbel extreme value distributions to estimate rainfall magnitudes at a 20% probability of exceedance (return period ≈ 5 years) for the months of July to October. The results indicate that the Gumbel distribution produced comparatively higher design rainfall estimates, followed by Log-Normal and Normal distributions, making it more suitable for extreme event analysis in the study region.
Frequency and Trend Analysis of Rainfall Data for Kochi, Kerala, India
Rainfall is a crucial component of many engineering designs, including hydraulic structures, water conservation structures, bridges and culverts, canals, storm water sewers, and road drainage systems. To estimate the relevant input value for crop planning as well as engineering structure design and analysis, a thorough statistical analysis of each region is necessary. The current study includes statistical analysis, namely frequency analysis of Kochi's seasonal rainfall data. Weibull's method is used to evaluate the designed value of rainfall based on daily rainfall data collected over a 20-year period. The mean, standard deviation, and coefficient of variation are computed to estimate the fluctuation in rainfall. The moving average method and probability distribution method are used to analyze the rainfall trend in annual and monthly time series. Over the course of these 20 years, September had the lowest mean monthly rainfall (59.16 mm), while June had the highest mean monthly rainfall (899.13 mm). June had the lowest coefficient of variation (CV), followed by July, while September had the highest coefficient of variation (CV). For the entire time period under consideration, it was found that the monthly rainfall for the selected Kochi was lower in September than in the other months. June and July had higher monthly rainfall than any other month over the whole study period.
Climate Change Impact on Fresh Water Flow and Hydropower in the Himalayan Region
This paper examines how climate change is reshaping the hydrological dynamics and hydropower infrastructure development in the Upper Ganges Basin of Uttarakhand, India. Drawing on field observations, consulting project experience, and multi-methodology hydrological analysis, the study identifies critical vulnerabilities affecting hydropower projects: sediment accumulation from landslides, Glacial Lake Outburst Floods (GLOFs), avalanche events, and seismicinduced erosion. Two major disaster events, the 2013 Kedarnath floods and the 2021 Chamoli rockslide-triggered flash flood, serve as case studies demonstrating compounding climate and structural hazards. The analysis emphasizes the urgent need for integrated risk assessment, real-time monitoring systems, and adaptive design standards for sustainable hydropower development in high-mountain regions vulnerable to rapid climate change.
Modeling Stage–Discharge and Runoff–Sediment Dynamics using Soft Computing and Statistical Approaches in the Nagavali River Basin, Andhra Pradesh
Soil and water are critical natural resources underpinning agricultural productivity and ecological sustainability, particularly in monsoon-dominated regions such as the Nagavali River basin in eastern India. Effective estimation of runoff additionally and sediment yield hand in hand is essential for workable watershed planning, hydraulic structure design, and sediment management, yet remains evergreen challenging due to the stochastic and nonlinear nature of hydrological processes. The Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), two data-driven soft computing techniques, are assessed in this study in conjunction with Multiple Linear Regression (MLR) and Sediment Rating Curve (SRC) methods for modeling daily stage–discharge and runoff–sediment relationships. The Water Resources Information System (WRIS) provided monsoon-season stage, discharge, and suspended sediment content data for a total of twelve years (2001–2012), with 2001–2009 used for training and 2010–2012 for testing. While ANFIS models used Gaussian and triangular membership functions with hybrid learning, ANN models used feed-forward back-propagation with Levenberg–Marquardt optimization. RMSE, correlation coefficient (r), coefficient of efficiency (CE), and pooled average relative error (PARE) were applyed to evaluate the model's performance. Results indicate that soft-computing models outperform traditional approaches for both runoff and sediment prediction. ANFIS with triangular membership functions demonstrated the highest accuracy, followed by doublehidden-layer ANN. MLR provided acceptable results, whereas SRC exhibited limited capability due to pronounced nonlinearity in sediment–runoff dynamics.
Status of Energy Used Efficiency and Farm Mechanization Level for Wheat Production in District Jaunpur, Uttar Pradesh
To meet the rising demand for food, wheat production, which accounts for about 25% of the world's cereal crop area, must be increased. Since wheat yield is directly correlated with energy inputs, the current study aims to investigate and optimize energy use patterns in wheat production throughout the Jaunpur district of Uttar Pradesh in order to achieve greater productivity. The study included six tehsils: Jaunpur (Sadar), Badlapur, Kerakat, Machhlishahar, Mariahu, and Shahganj. The energy output from grain and straw as well as the energy used for land preparation, sowing, irrigation, fertilizer application, harvesting, and threshing were among the many energy and mechanization indicators that were taken into account. In the district under study, the total energy input for wheat cultivation was estimated to be 16,658.58 MJ/ha, while the energy output was 98,181.3 MJ/ha. At 8,257.27 MJ/ha, chemical fertilizers accounted for the largest share of energy inputs. Of these, nitrogen shared roughly 7,415.99 MJ/ha, followed by phosphorus. Harvesting and threshing operations used 1,877.23 MJ/ha of energy, whereas irrigation used 3,313.12 MJ/ha. The region's wheat production had an EUE of 5.89, which further indicates that energy output is nearly six times greater than energy input. EUE was greatly impacted by farm size. Large farms had the highest value (6.36), medium-sized farms ranged from 5.66 to 5.96, and small farms had the lowest value (5.64), indicating a declining trend with smaller landholdings.
Probability Analysis of Annual Maximum Daily Rainfall for Karnataka, India
Daily rainfall data for 22 years (2000–2022) were obtained from the Global Weather Data for SWAT and analyzed to assess annual and monthly rainfall variability. The annual series was examined to determine maximum and minimum rainfall values, while the monthly series was processed to evaluate monthly patterns. Statistical parameters such as standard deviation and coefficient of variation (CV) were also calculated for the study period. Results showed that the annual maximum daily rainfall varied widely between 1 mm and 10.55 mm, indicating substantial temporal variability. Monthly analysis revealed that October consistently received the lowest rainfall across all study years, whereas July and August recorded the highest rainfall, highlighting the dominance of the monsoon season. Among the months, August exhibited the lowest CV (58.35%), followed by September (61.82%), suggesting comparatively stable rainfall patterns during these months. In contrast, June recorded the highest CV (75.04%), indicating greater inter-annual variability. Further, frequency analysis showed that the maximum daily rainfall values estimated using both Normal and Log-Pearson Type III distributions at a 20% probability of exceedance were highest during the months of July to October.
Design, Development and Performance Evaluation of Manually Operated Groundnut Planter
Groundnut is one of the key food crops for human and animal consumption and raw materials in industries. In this work, a manually operated groundnut planter was designed to require less operating force to reduce the seed damage and missing rates. During the laboratory calibration, the average seed rate in the planter was 66.40 kg/ha. The mean percentage of seed damage assessed from the planter is 0.45%, indicating the gentleness of the seeds during its operation. Minimum and maximum variations in the seed spacing are 19.4 cm and 19.7 cm, respectively, which is considered reasonable spacing. In addition, the missing index, which is a measure of the percentage hills where seed was missing, was 2.7% and 4.3% respectively for 1.5 km/h and 2.5 km/h forward speed, which gave better performance at lower operating speeds. Field testing of the manual groundnut planter showed satisfactory performances. Its mean seed rate was 65.08 kg/ha, while the missing index was 3.67%. The theoretical and actual field capacities recorded were 0.09 ha/h and 0.077 ha/h, respectively, with a field efficiency of 86.57%. The depth of seed placement, which fell within 4.1 cm, is appropriate for planting groundnut. It was also found that use of this equipment was easy, both for males and females, which shows its practical utility for smallholder farmers. A manually operated groundnut planter has been fabricated with locally available materials; hence, it was relatively inexpensive and simple to construct.
Probability and Trend Analysis of Rainfall Data of Shillong District
This study analyses 15 years (2002–2016) of daily rainfall data for Shillong, located in northeastern India, obtained from the Global Weather Data repository developed for the SWAT model. The primary objective was to evaluate the variability, trends, and statistical characteristics of annual and seasonal rainfall. Descriptive statistical analysis was carried out to determine annual maximum and minimum rainfall, monthly rainfall variability, standard deviation, and coefficient of variation. The annual rainfall exhibited substantial variability, ranging from 0.04 mm to 1609.38 mm. The lowest mean monthly rainfall occurred in 2006 (115.3 mm), followed by 2013 (123.4 mm) and 2005 (151.2 mm), whereas the highest was recorded in 2004 (255.3 mm). The computed standard deviation (326.6) and coefficient of variation (127.9%) indicated a high degree of variability in rainfall distribution. Returnperiod analysis classified Shillong under normal rainfall conditions, with the maximum rainfall event corresponding to a return period of 24 years. Trend analysis based on slope (m) values revealed an increasing rainfall trend during August (18.257 mm/year) and September (11.537 mm/year), while June (–19.338 mm/year), July (–25.535 mm/year), and October (–13.815 mm/year) exhibited declining trends. The annual slope value (–28.894 mm/year) indicated an overall decreasing trend in annual rainfall over the study period.
Assessment of Physico-Chemical Property of the Gomti River Lucknow, Uttar Pradesh, India
Determining the current condition of the water in the Gomti River along the Lucknow section was the aim of this investigation. Sewage pollution, levels of organic compounds, different heavy metals, and physico-chemical properties have all been thoroughly examined across Lucknow. The Gomti barrage is below, while Gaughat is upstream.Samples are moved from regions upstream to those downstream.Total solids, total dissolved solids, total suspended solids, temperature, pH, As, Cd, nitrate, nitrite, chloride, iron, copper, hardness, and dissolved oxygen (DO) are all examined in water samples. According to the study's findings, the Gomti River is contaminated by numerous drains that dump untreated household and industrial garbage into the river. Other reasons to remove solid waste at pumping stations include the fact that most branch and trunk sewers don't work properly or that the pumping station occasionally malfunctions, causing sewage waste to be bypassed and dumped straight into the Gomti River. According to the report, the discharge of untreated garbage from roughly 26 major drains along the Gaughat to Gomti barrage has caused the water quality to deteriorate. The Gomti River near Lucknow, also known as Gaughat, is a river that has a minimum pH of 7.55 and a maximum dissolved oxygen content of 3.33 mg/lit. But due to presence of 26 drains the dissolved oxygen level decreases with its stretch and showed minimal DO at Laxmanamela Park.
Agronomical Impacts and Performance of Combined Harvester with Integrated Straw Management System
The evolution of combine harvesters from manual and labour-intensive methods to advanced, technology-driven machines has greatly impacted agricultural practices, particularly in straw management. Historically, crop harvesting involved significant human effort, with straw often burned or left in the field, leading to environmental concerns and soil degradation. The advent of combine harvesters in the 20th century revolutionized harvesting efficiency, but early models did not address effective straw management. Today’s modern combine harvesters are equipped with sophisticated systems that chop, spread, or bale straw, offering sustainable solutions that enhance soil health by returning organic matter, improving soil fertility, and preventing erosion. Additionally, advanced technologies such as GPS and sensors allow for real-time optimization of harvesting and straw management processes. These innovations not only reduce environmental impacts, such as straw burning, but also provide economic benefits by enabling the reuse of straw for purposes like animal bedding or biomass fuel. The integration of these modern straw management techniques has made farming more efficient and environmentally sustainable, highlighting the significant progress from the labour-intensive past to the sustainable practices of today.