Cyanobacterial harmful algal blooms (cHABs) pose increasing ecological and public health risks in shallow lakes subject to nutrient enrichment and climate change. This research describes and evaluates a three-dimensional hydrodynamic ecological modeling framework for Fairy Lake, Ontario, using MIKE 3 FM coupled with ECO Lab to simulate lake circulation, thermal structure, nutrient dynamics, and cyanobacteria (PC3) concentrations, with model calibration using 2022 data observations and validation using 2023 data. Hydrodynamic performance showed moderate agreement for lake levels (NSE 0.47–0.52) and strong predictive capability for water temperature (NSE up to 0.96 across depths). Nutrient simulations reproduced seasonal nitrate and phosphate trends, with sediment parameter adjustments to stabilize internal loading dynamics. Cyanobacteria simulations captured seasonal bloom timing and spatial variability between inflow and central basin zones during the calibration and validation periods. The results demonstrate that integrated 3D hydrodynamic ecological modeling reproduces seasonal bloom dynamics in shallow polymictic lakes employing calibrated models and subsequent data, to predict sediment nutrient processes and periodic reassessments. As a result, this framework provides a quantitative basis for evaluating bloom behavior and the ability to predict management scenarios able to test changing climatic and nutrient conditions.

The enormous groundwater demand globally have possessed a threat to groundwater security. Rajasthan being one of the water deficit states of India is pivotal to determine its groundwater potential zone for sustainability. Diverse thematic layers such as drainage density, landuse, rainfall, geology, geomorphology was prepared. The geomorphology, landuse, drainage and soil map was prepared using IRS-1D LISSІІІ satellite image. Through, appointing the relative weightage to the thematic maps the final groundwater potential map was prepared. A comparative analysis for the groundwater potential map and the groundwater level data was done for confirmation. The groundwater potential zone can be classified as high, medium or low. The positions of various existing wells along with the contour map of groundwater table can be draped with groundwater potential map. The groundwater table usually found flat over the regions of higher groundwater potential zones. Thus, with the increasing demography along with rising demand the wells should be laid over the area with higher potential. The remote sensing and GIS can be efficiently and smoothly used to determine the groundwater potential zones with suitable sites for well construction. Keywords: Groundwater sustainability, Remote Sensing, Geographic Information System, Thematic layers, Groundwater potential map.

Industrial contamination events have repeatedly demonstrated the consequences of mischaracterizing contaminant behavior and exposure pathways in environmental systems. Despite advances in modeling tools, decision-making errors often arise from incomplete understanding of contaminant fate and transport. This study presents a structured screening framework based on eight key physicochemical coefficients that govern the behavior of chemical contaminants in air, water, and soil systems. The identified parameters, partition coefficients ( K o w and K o c ), Henry’s law constant, aqueous solubility, hydrolysis rate, acid dissociation constant (pKa), flash point, and biodegradation potential, collectively characterize volatility, mobility, persistence, and phase distribution. By systematically evaluating these coefficients before selecting modeling approaches, the framework enables identification of dominant transport mechanisms and likely exposure pathways, reducing the risk of inappropriate model selection and misinterpretation of environmental risk. The proposed methodology integrates contaminant properties with environmental media interactions to support more defensible engineering assessments and improve disaster prevention strategies. Application of the framework demonstrates how early-stage screening can clarify whether contaminants are likely to remain localized, migrate through groundwater, volatilize into the atmosphere, or persist in sediments, thereby guiding monitoring, mitigation, and remediation planning. This framework can be used as a useful tool to strengthen risk assessment processes and enhancing resilience against future chemical contamination events.

Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.

Accurate stream flow forecasting is essential for flood risk management and preparedness. This study compares two forecasting approaches: (a) the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Regressors (SARIMAX), a classical statistical model, and (b) Prophet, a decomposable time-series forecasting model that incorporates seasonality and exogenous predictors. Forecasts were generated for 15-day and 3-day horizons and evaluated using uncertainty bounds, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). Results indicate that SARIMAX was less effective at capturing the observed variability, producing wide uncertainty (177.7%) and high errors (MAE = 153.73; RMSE = 207.10) with a negative R² (–4.42). At shorter horizons, its performance remained limited (uncertainty = 28.04%; MAE = 61.52; RMSE = 94.88; R² = –0.14). In contrast, Prophet achieved significantly lower uncertainty (16%), high accuracy (R² = 0.95), and exceptional performance on short-term forecasts (R² = 0.99). Conventional procedures such as SARIMAX have long been relied upon by engineers for their interpretability, and remain important as part of a strategy; however, they fail to represent nonlinear dynamics and exogenous influences now captured effectively by AI-based models. These findings highlight Prophet’s superiority across horizons and its promise for enhancing operational flood forecasting through its ability to effectively capture non-linear dynamics and exogenous influences.

Reliable flood forecasting in snow-fed basins requires explicit consideration of snowpack dynamics, which strongly influence streamflow variability. This study enhances flood prediction by integrating snow water equivalent from multiple mountain stations and validating across three major flood events (2008, 2012, and 2013) in the Bow River Basin, Canada. Results show that while forecasts made 7 days ahead provide early flood signals, they carry notable uncertainty, with vertical peak flow errors exceeding 7% during extreme conditions. For the 2013 extreme flood event, 5-day forecasts demonstrate markedly improved performance, achieving NSE values above 0.90, reducing root mean square error from 41.7 to 1.61 units, and lowering vertical error to below 1%. Three-day forecasts perform even better, achieving NSE values of 0.99 and minimal timing error. These results indicate that the 5-day horizon represents the optimal balance between early warning capability and actionable reliability. The findings also highlight that incorporating snow water equivalent significantly enhances prediction accuracy, improving NSE by 0.20–0.28 and reducing magnitude error by more than 50% compared to models without snowpack data. Overall, this work underscores the importance of integrating mountain snowpack information into flood forecasting frameworks to strengthen early warning systems and operational decision-making.