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.
Integrated GIS–MCDA (AHP) Framework for Groundwater Potential Mapping in Humid, Structurally Complex Watersheds
0 citations
Silwal, A., Dahal, D., Poudel, B., Pokhrel, B., Shrestha, S., Kalra, A.
Hydroecology and Engineering (2026)
This study presents an integrated GIS and multi-criteria decision analysis framework using the Analytical Hierarchy Process to map groundwater potential zones in humid, structurally complex watersheds, offering a replicable approach for sustainable groundwater resource management.
A Comprehensive Review of Machine Learning and Deep Learning Methods for Flood Inundation Mapping
1 citations
Silwal, A., Subedi, A., Tamrakar, R., Dahal, K., Dahal, D., Ekpetere, K. O., Zhran, M.
Earth (2026)
This comprehensive review systematically evaluates machine learning and deep learning techniques applied to flood inundation mapping, identifying strengths, limitations, and future directions to guide researchers and practitioners in adopting data-driven approaches for flood risk assessment.
Assessing Flood Susceptibility Using a Data-Driven, GIS-Based Frequency Ratio Model
1 citations
Sewa, R., Poudel, B., Shrestha, S., Dahal, D., Kalra, A.
Atmosphere (2026)
Using a GIS-based frequency ratio model driven by multi-source spatial data, this study maps flood susceptibility zones with high accuracy, providing actionable insights for land-use planning and flood risk reduction in vulnerable regions.
A Review on Climate Change Impacts on Freshwater Systems and Ecosystem Resilience
12 citations
Dahal, D., Bhattarai N., Silwal A., Shrestha, S., Shrestha B., Poudel B., Kalra, A.
Water (2025)
This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. 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.
Machine Learning-Based Flood Risk Assessment in Urban Watershed: Mapping Flood Susceptibility in Charlotte, North Carolina
7 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.
Developing a Composite Drought Indicator Using PCA Integration of CHIRPS Rainfall, Temperature, and Vegetation Health Products for Agricultural Drought Monitoring in New Mexico
9 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.
Assessing Flood Susceptibility and Frequency Analysis in Himalayan River Basins: A GIS-Based Multi-Criteria Approach
2 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.
Flood Susceptibility Analysis with Integrated Geographic Information System and Analytical Hierarchy Process: A Multi-Criteria Framework for Risk Assessment and Mitigation
38 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 the Performance of HEC-HMS and SWMM Models for Rainfall - Runoff Simulation for Urban Watersheds
6 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.
The Role of Reclaimed Water in Urban Flood Management: Public Perception and Acceptance
5 citations
Dahal, D., Shrestha, S., Poudel, B., Banjara, M., Kalra, A.
Earth Science Research (2024)
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.
Application of Machine Learning and Hydrological Models for Drought Evaluation in Ungauged Basins Using Satellite-Derived Precipitation Data
13 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
18 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
3 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.
Analyzing Climate Dynamics and Developing Machine Learning Models for Flood Prediction in Sacramento, California
13 citations
Dahal, D., Magar, B. A., Aryal, A., Poudel, B., Banjara, M., Kalra, A.
Hydroecology 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.
