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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Industrial and municipal wastes, agricultural contamination owing to pesticides and chemical hazards, seawater intrusion in coastal areas, and other factors damage groundwater. In several towns and industrial clusters across India, is becoming a rising subject of concern. Groundwater is difficult to contaminate, but once contaminated, it is difficult to clean up. It is critical to attain this goal using a variety of aquifer vulnerability assessment approaches. All of these strategies rely on process models as well as statistical or overlay index methodologies. Groundwater vulnerability is a major topic of discussion due to declining groundwater levels and rising contamination, posing a serious threat to the environment and water sources. To identify the risk and to assess the vulnerability extensive research has carried out among them all the methods are worked based on the different parameters and different indexes. DRASTIC method is one of the most important and accurate method of overlay and index method for the assessment of groundwater vulnerability. This research study is a systematic analysis of the available research articles on the applications of DRASTIC and Modified DRASTIC (DRASTIC-L) performance management process on Geographical Information Systems (GIS).This research also reveals research gaps in the various groundwater vulnerability assessment approaches, as well as their limits and hypotheses.This study discovered that integrating GIS with DRASTIC is the most effective and accurate way for determining groundwater vulnerability. In the agricultural, arid, semi-arid, and basaltic zones, the modified DRASTIC model also outperforms the traditional DRASTIC model.

Drought forecasting is being considered an important tool to help understand the rainfall pattern and climate change trend. Drought is a prolonged period of months or years in which an area, whether surface water or groundwater, becomes insufficient in its water supplies. Drought is considered as most difficult but least known environmental phenomenon, impacting more persons than any other. There are several indices used to classify droughts. For this study, precipitation-based drought indices are considered (i.e., SPI, RAI and Percentage Departure of Rainfall). The objective of the research is to examine and determine the possible rainfall trends over the Jalore district of South-West Rajasthan in Luni river basin. In this research, trend analysis using the rainfall data from the years 1901 to 2021 was carried out on monthly, seasonal and annual basis. To define the current trend path, the Mann-Kendall test and Sen's slope estimator test were used. In order to detect the trend and its change in magnitude over a particular period of time, Sen's slope estimator was used. During the southwest monsoon, declining rainfall leads to short-term meteorological droughts, which have severe effect on the agriculture sector and Jalore district's water supplies, while rising rainfall during other seasons tends to mitigate the severity of drought. The result of research reveals that there is rise of pre-monsoon and post-monsoon rainfall, but it also depicts a fall in the annual rainfall which reflects in reduced Winter and S-W monsoon rainfall.

Floods are one of the world's most destructive natural disasters, taking more lives and causing more infrastructural damage than any other natural phenomenon. Floods have a significant economic, social, and environmental impact in developing countries like India. As a result, it is essential to address this natural disaster to mitigate its effects. The lower Narmada basin has experienced numerous floods, including severe flooding in 1970, 1973, 1984, 1990, 1994, and 2013. The objective of the present study is to use flood frequency analysis to anticipate peak floods and prepare flood inundation maps for the lower Narmada River reach. The flood frequency analysis was carried out using Gumbel's and Log-Pearson Type III Distribution methods. The hydrodynamic simulation was performed using HEC-RAS v6.0 to prepare flood inundation maps for predicted flood peaks. The result shows that the Log-Pearson Type-III distribution method gives good results for the lower return period while Gumbel's method gives good results for the higher return period. The hydrodynamic model results indicate that as the return period increases, the area of the high-risk zone increases while the area of the low-risk zone remains almost constant. The present study concludes that the existing embankment system on the banks of the Narmada River is not sufficient for significant floods. The developed maps will be helpful to government authorities and individual stakeholders to decide the flood mitigation measures.