Climate variability mainly the annual air temperature and precipitation have received great attention worldwide. The magnitude of these climate variability changes with the variation in locations. Rajasthan comes under the arid and semi-arid zone of India in which monsoon is a principal element of water resource. Due to erratic and scanty rainfall in this zone, agriculture is totally dependent on the monsoon. The objective of the present study is to assess the meteorological drought characteristics using Drought Indices Calculator DrinC from the historical rainfall records of the Barmer district of Rajasthan state by employing the criterion of percentage departure (D%), rainfall Anomaly index (RAI) and standardized precipitation index (SPI). Trend analysis of seasonal and extreme annual monthly rainfall was carried out for the Barmer district of Rajasthan state using the data period between 1900 and 2002 at the 5% level of significance. Sen's slope estimator was also applied to identify the trend. Temporal analysis is useful to predict and identify the possible drought severity and its duration in the study region. It also helps to understand its effect on ground water recharge and increasing the risk of water shortage. Trend analysis of rainfall over 102 years shows an increasing trend in pre-monsoon, post monsoon, southwest monsoon and annual rainfall and decreasing trend in winter rainfall. Through this study, policy makers and local administrators will be benefitted which will help them in taking proactive drought relief decision in the drought-hit regions.

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling.

The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals.

Scour is now one of the main problems for river as well as for coastline engineering. Bridges are the vital structure which must be designed to prevent failure against scour effect. Scour hole is liable without warning for the failure of bridge. The main significant issues in hydraulic and river Engineering is to determine the connection between parameters affecting the maximum and minimum depth of scour. The scour depth in the alluvial stream below the river bed differs based on the flows, pier shape, pier size and sediment characteristics. Dual bridges of basically same structure are parallel placed and only a small distance away from existing bridge either on upstream or downstream side. Naturally, the backwater generated by dual bridges is bigger than that of a single bridge but lower than the value resulting from separate consideration of the two bridges. In the present work, hydraulic model is used to simulate stability of bridge in study area namely as ‘Sardar Bridge’ on Tapi river. Scour profiles for various flood events have been assessed on particular bridge. The velocity of flow is used to estimate depths of scour at different piers and abutments. Estimating depth of the scour during the design can significantly decrease the overall cost of bridge foundation construction. Result from present study shows that construction of new bridge should be proposed on the upstream side rather than downside side of existing bridge. By doing so, hydraulic stability of the existing bridge is ensured.

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