Featured Work

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Nature Based Solutions to Floods & Droughts

Flood and drought reduction and water quality mediation capacities of natural infrastructures -- wetlands, depressions, and other small water systems -- are largely disregarded in conventional environmental modeling and management practices. We are filling this knowledge gap. 

Related publications:

• Rajib et al., 2020. DOI: 10.1029/2019WR026561​
• Golden et al., 2021. DOI: 10.1088/1748-9326/ac1fbc
• Golden et al., 2019. DOI: 10.1021/acs.est.8b07270

Wildfire Impacts on Water Resources

We are developing HydroFlame that integrates emerging satellite remote sensing data with hydrologic models and AI, providing watershed managers the first-of-its-kind platform to predict, analyze, and visualize how wildfires impact water availability and water quality.  We are also collecting real-time field data to validate our models for recent events including the catastrophic 2025 Los Angeles wildfires. 

Related publications:

• Uddin & Rajib, 2024. DOI: 10.1016/j.crsus.2025.100433
• Bhattacharjee et al., 2024. DOI: 10.1038/s023-02382-x​​

Human Alterations of Natural Floodplains 

We are quantifying human alterations of natural floodplains in the world's major river basins. Our study is the first to discover a significant 35,000 square kilometers loss of natural floodplains in the Mississippi River Basin between 1941 and 2000, and more than 600,000 square kilometers around the world between 1992 and 2019.  

Related publications:

• Rajib et al., 2025. DOI: 10.1016/j.crsus.2025.100433
• Rajib et al., 2023. DOI: 10.1038/s41597-023-02382-x
• Rajib et al., 2021. DOI: 10.1038/s41597-021-01048-w​​

Large-scale Low-complexity Flood Modeling

There is little practical value in a so-called large-scale flood model when that model disregards numerous small tributaries, produces data only for the major rivers, or is too challenging to run in near real-time during an actual flood emergency. To avert this, we are making "large-scale low-complexity" flood predictions feasible. 

Related publications:

• Rajib et al., 2020. DOI:10.1016/j.jhydrol.2019.124406  
• Dhote et al., 2023. DOI:10.1016/j.jhydrol.2023.129309
• Maidment et al., 2016. DOI: 10.4211/technical.20161019

Watershed Modeling with Big Data

Remotely sensed Earth Observations can limit watershed models' tendency to give "right answers for wrong reasons". We developed an advanced SWAT modeling framework which automatically assimilates these emergent datasets.

Related publications:

• Rajib et al., 2020. DOI: 10.3390/rs12132148 ​ 
• Rajib et al., 2018. DOI:10.1029/2017WR021147
• Wang et al., 2025. DOI:10.1016/j.envsoft.2025.106618

Cyber-infrastructure for Hydrology 

We develop innovative ways for web-based access, integration, simulation, and visualization of hydrologic data, metadata, and models. Our work contributes to Findable, Accessible, Interoperable, and Reproducible (FAIR) geoscience.

Related publications:​

• Rajib et al., 2022. DOI:10.1016/j.envsoft.2022.105561
• Rajib et al., 2016. DOI:10.1016/j.envsoft.2015.10.032 

Latest update: April 22, 2025