This collection highlights peer-reviewed research advancing water resource science, climate resilience, and AI-driven hydrologic modeling. These studies deploy innovative methodologies that improve prediction, assessment, and management of water extremes and droughts in complex environments.
Developing a Composite Drought Indicator Using PCA Integration of CHIRPS Rainfall, Temperature, and Vegetation Health Products for Agricultural Drought Monitoring in New Mexico
0 citations
Poudel, B., Dahal, D., Shrestha, S., Sewa, R., Kalra, A.
Atmosphere (2025)
By combining PCA techniques with multi-source climate and vegetation data, this study created a composite drought indicator that provides an improved early warning system for agricultural drought, facilitating optimized irrigation planning and water allocation in drought-prone US agricultural regions.
Machine Learning-Based Flood Risk Assessment in Urban Watershed: Mapping Flood Susceptibility in Charlotte, North Carolina
0 citations
Shrestha, S., Dahal, D., Bhattarai, N., Regmi, S., Sewa, R., Kalra, A.
Geographies (2025)
This research applied advanced machine learning algorithms coupled with spatial analysis to map flood susceptibility across Charlotte, supporting urban planners with data-driven tools for flood mitigation strategies in rapidly developing southeastern US cities.
The Role of Reclaimed Water in Urban Flood Management: Public Perception and Acceptance
0 citations
Dahal, D., Shrestha, S., Poudel, B., Banjara, M., Kalra, A.
Earth Science Research (2025)
The investigation of reclaimed water's role in flood management incorporated social science methods to assess public perception, enabling stakeholders to address community concerns and design effective urban water reuse programs that balance flood control and sustainability goals.
Assessing the Performance of HEC-HMS and SWMM Models for Rainfall - Runoff Simulation for Urban Watersheds
0 citations
Kalra, A., Shrestha, S., Dahal, D., Banjara, M., Poudel, B., Gupta, R.
World Environmental and Water Resources Congress (2025)
Through comparative simulation of HEC-HMS and SWMM models, this study identified conditions for optimal model application to urban rainfall-runoff scenarios, providing guidance to engineers for improving flood infrastructure design and regulatory compliance.
Flood Susceptibility Analysis with Integrated Geographic Information System and Analytical Hierarchy Process: A Multi-Criteria Framework for Risk Assessment and Mitigation
6 citations
Shrestha, S., Dahal, D., Poudel, B., Banjara, M., Kalra, A.
Water (2025)
Utilizing a GIS-Analytical Hierarchy Process framework, the study developed a scalable flood susceptibility assessment tool that integrates multiple risk factors, offering policymakers a comprehensive tool to prioritize flood resilience investments.
Assessing Flood Susceptibility and Frequency Analysis in Himalayan River Basins: A GIS-Based Multi-Criteria Approach
0 citations
Shrestha, B., Rajbhandari, E., Kayastha, R. B., Shrestha, S., Dahal, D.
Kathmandu University Journal of Science Engineering and Technology (2025)
Employing GIS-based multi-criteria analysis in a mountainous basin, the methods from this study can be adapted for similarly complex terrain in the US, aiding flood risk mitigation efforts in vulnerable high-elevation watersheds.
Developing a Composite Drought Indicator Using PCA Integration of CHIRPS Rainfall, Temperature, and Vegetation Health Products for Agricultural Drought Monitoring in New Mexico
0 citations
Poudel, B., Dahal, D., Shrestha, S., Sewa, R., Kalra, A.
Atmosphere (2025)
This study developed a composite drought indicator leveraging PCA on multiple climatic and vegetation datasets specific to New Mexico, enhancing detection and monitoring of agricultural droughts in arid US regions. The index improves early warning capacities and supports sustainable water management.
Analyzing Climate Dynamics and Developing Machine Learning Models for Flood Prediction in Sacramento, California
3 citations
Dahal, D., Magar, B. A., Aryal, A., Poudel, B., Banjara, M., Kalra, A.
Hydrology and Engineering (2024)
Utilizing machine learning integrated with climate dynamics, this paper developed precise flood prediction models focused on the Sacramento watershed, enhancing urban flood risk management and informing disaster preparedness for climate-impacted communities in California.
Application of Machine Learning and Hydrological Models for Drought Evaluation in Ungauged Basins Using Satellite-Derived Precipitation Data
3 citations
Parajuli, A., Parajuli, R., Banjara, M., Bhusal, A., Dahal, D., Kalra, A
Climate (2024)
Integrating satellite-derived precipitation with hydrological models using machine learning, the study offers a new approach for drought assessment in ungauged or data-limited areas, enhancing water supply reliability and drought resilience in similar remote US basins.
Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models
6 citations
Poudel, B., Dahal, D., Banjara, M., Kalra, A.
Forecasting (2024)
Employing machine learning to improve drought index forecasts, this work enhances meteorological drought prediction precision, contributing to more reliable drought preparedness and management decisions in US water resource systems.
Evaluation and Comparison of Curve Numbers Based on Dynamic Land Use and Land Cover Changes
0 citations
Gautam, M., Bhattarai, N., Magar, B. A., Dahal, D., Shrestha, S., Poudel, B., Hasnat, A.
Current Trends in Civil & Structural Engineering - CTCSE (2024)
By examining land use changes in a US-relevant context, this study refines runoff estimation through improved curve number methods, supporting accurate hydrologic modeling for urban stormwater and agricultural runoff management.
