Dewasis Dahal

Flood inundation mapping (FIM) is essential in disaster risk management, infrastructure planning, and climate adaptation. Traditional hydrodynamic models, such as the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and LISFLOOD-Floodplain (LISFLOOD-FP), provide physically interpretable flood simulations but are often data- and computation-intensive and difficult to scale across regions. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as data-driven alternatives that leverage remote sensing observations, digital elevation models (DEMs), and hydro-climatic datasets to enable scalable and near-real-time flood mapping. Our review synthesizes recent advances in ML-based flood inundation mapping, categorizing methods into traditional machine learning techniques (e.g., Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB)), deep learning architectures (e.g., Convolutional Neural Networks (CNNs), U-Net, Long Short-Term Memory networks (LSTM)), and emerging hybrid and physics-informed frameworks. We evaluate model performance across flood extent and flood depth estimation tasks, highlighting strengths, limitations, and common benchmarking practices reported in the literature. The review identifies key challenges related to model interpretability, data bias, transferability, and regulatory acceptance, and highlights recent progress in explainable artificial intelligence (XAI), uncertainty-aware modeling, and physics-informed learning as pathways toward operational adoption. By unifying terminology, performance metrics, and methodological comparisons, this review provides a coherent framework for advancing trustworthy, scalable, and decision-relevant flood inundation mapping under increasing climate-driven flood risk.

Flooding is one of the major natural disasters that have a major impact on urban areas due to the increasing intensity of factors like extreme weather conditions, climate change, and unplanned urbanization. Considering Cook County, Illinois, the rapid development of the region, flat topography, and the induced rainfall extremes from climate change increase the potential risk of flooding when interacting with dense urban exposure and infrastructure. This study employed the Frequency Ratio (FR) model in a GIS environment to create a high-resolution flood susceptibility map of the county. The map was developed using 281 historical flood points collected from several authoritative sources, such as National Oceanic and Atmospheric Administration (NOAA) Storm Events Database records, Federal Emergency Management Agency (FEMA) Flood Insurance Study (FIS) and Flood Insurance Rate Map (FIRM)-based FIRMette products, and U.S. Geological Survey (USGS) flood-inundation studies. Thirteen conditioning factors, including land use, elevation, slope, soil drainage, rainfall, and distance to the stream, were used to calculate FR values and to develop the Flood Susceptibility Index (FSI). The resulting FSI was grouped into four susceptibility zones: low, medium, high, and very high. The findings indicated that more than 64% of Cook County has a high and very high risk of flood susceptibility, particularly in the vicinity of major river corridors. The model was validated using testing data with a 91.4% prediction accuracy, which also demonstrated the reliability and applicability of the FR model in the urban flood susceptibility assessment. The map serves as a valuable tool for risk-based urban planning and design of flood mitigation infrastructure in one of the most populated counties in the United States.

Climate change is fundamentally transforming global water systems, affecting the availability, quality, and ecological dynamics of water resources. This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. Rising global temperatures are disrupting thermal regimes in rivers, lakes, and ponds; intensifying the frequency and severity of extreme weather events; and altering precipitation and snowmelt patterns. These changes place mounting stress on aquatic ecosystems, threaten water security, and challenge conventional water management practices. The paper also identifies key vulnerabilities across diverse geographic regions and evaluates adaptation strategies such as integrated water resource management (IWRM), the water, energy and food (WEF) nexus, ecosystem-based approaches (EbA), the role of advanced technology and infrastructure enhancements. By adopting these strategies, stakeholders can strengthen the resilience of water systems and safeguard critical resources for both ecosystems and human well-being.

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones.

Drought indices are important resources for monitoring and warning of drought impacts. However, regions like New Mexico, which are highly vulnerable to drought, as identified by the United States Drought Monitor (USDM), lack a comprehensive drought monitoring system that integrates multiple agrometeorological variables into a single indicator. The purpose of this study is to create a Combined Drought Indicator for New Mexico (CDI-NM) as an indicator tool for use in monitoring historical drought events and measuring its extent across the New Mexico. The CDI-NM was constructed using four key variables: the Vegetation Condition Index (VCI), temperature, Smoothed Normalized Difference Vegetation Index (SMN), and gridded rainfall data. A quantitative approach was used to assign weights to these variables employing Principal Component Analysis (PCA) to produce the CDI-NM. Unlike conventional indices, CDI-NM assigns weights to each variable based on their statistical contributions, allowing the index to adapt to local spatial and temporal drought dynamics. The performance of CDI-NM was evaluated against gridded rainfall data using the 3-month Standardized Precipitation Index (SPI3) over a 17-year period (2003–2019). The results revealed that CDI-NM reliably detected moderate and severe droughts with a strong correlation (R² > 0.8 and RMSE = 0.10) between both indices for the entire period of analysis. CDI-NM showed negative correlation (r < 0) with crop yield. While promising, the method assumes linear relationships among variables and consistent spatial resolution in the input datasets, which may affect its accuracy under certain local conditions. Based on the results, the CDI-NM stands out as a promising instrument that brings us closer to improved decision-making by stakeholders in the fight against agricultural droughts throughout New Mexico.

Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP). In this study, ten flood causative factors are employed to generate flood-prone zones. AHP, a form of weighted multi-criteria decision analysis, is applied to assess the relative impact weights of these flood causative factors. Subsequently, these factors are integrated into ArcGIS Pro (Version 3.3) to create a flood susceptibility map for the study area using a weighted overlay approach. The resulting flood susceptibility map classified the county into five susceptibility zones: very low (17.48%), low (41.89%), moderate (37.53%), high (2.93%), and very high (0.17%). The FEMA flood hazard map of Davidson County is used to validate the flood susceptibility map created from this approach. Ultimately, this comparison reinforced the accuracy and reliability of the flood susceptibility assessment for the study area using integrated GIS and AHP approach.

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. SPI values were calculated from the precipitation data obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a hydrological model for calculating discharge and drought in the overall basin. It uses random forest (RF) and support vector regression (SVR) as machine learning models for SSI prediction for time scales of 1- and 3-month periods, which are widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the machine learning model’s results. The results from the prediction model, hydrological model, and the station data show better correlation. The coefficients of determination obtained for 1-month SSI are 0.842 and 0.696, and those for the 3-month SSI are 0.919 and 0.862 in the RF and SVR models, respectively. These results also revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one, with the better prediction result being from the SVR model. The hydrological model-evaluated SSI has 0.885 and 0.826 coefficients of determination for the 1- and 3-month time durations, respectively. The results and discussion in this research will aid planners and decision-makers in managing hydrological droughts in basins.

The rising frequency and severity of droughts requires accurate monitoring and forecasting to reduce the impact on water resources and communities. This study aims to investigate drought monitoring and categorization, while enhancing drought forecasting by using three machine learning models—Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF). The models were trained on the study region’s historic precipitation and temperature data (minimum and maximum) from 1960 to 2021. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed for a time scale of 3, 6 and 12 months. The monthly precipitation data were used for creating lag scenarios and were used as input features for the models to improve the models’ performance and reduce overfitting. Statistical parameters like the coefficient of determination (R²), Mean Absolute Error (MAE), Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) were determined to evaluate the model accuracy. For forecasting, the SPEI3, ANN and SVM models show better performance (R² > 0.9) than the RF models when the 3-month lag data were used as input features. For SPEI6 and SPEI12, the 6-month lag and 12-month lag data, respectively, were needed to increase the models’ accuracy. The models exhibited RMSE values of 0.27 for ANN, 0.28 for SVM, and 0.37 for RF for the SPEI3, indicating the superior performance of the former two. The models’ accuracy increases as the lag period increases for SPI forecasting. Overall, the ANN and SVM models outperformed the RF model for forecasting long-term drought.

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