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Adnan Rajib

Has grant

Assistant Professor

The University of Texas at Arlington

United States

Research Interests

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About

Adnan Rajib is an Assistant Professor at the University of Texas at Arlington, USA. His research primarily focuses on hydrology, Earth observations, and floodplain dynamics, as evidenced by his recent works on hydrologic model evaluations and flood modeling errors. He is also engaged in advancing open science methodologies to enhance the accuracy of climate predictions and water resource management.

Recent Grants

Grant: Close

Making Post-wildfire Hydrologic Change Assessments Efficient by Developing A Web-based Remote Sensing-integrated Hydrologic Modeling Tool (Principal Investigator)

Open Date: 2021-12-01

Close Date: 2024-12-01

Grant: Close

Resilient Coastal Dune Ecosystems for Erosion and Habitat Protection (Co-Investigator)

Open Date: 2021-01-01

Close Date: 2023-01-01

Grant: Close

Evaluating Non-floodplain Wetlands for Flood-Risk Reduction and Nutrient Mediation in the Mississippi River Basin (Principal Investigator)

Open Date: 2020-01-01

Close Date: 2025-01-01

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Collaborators (9)

Virginia Smith

Assistant Professor

Villanova University

UNITED STATES

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Shih-Chieh Kao

Oak Ridge National Laboratory

UNITED STATES

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Sagy Cohen

UNITED STATES

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Fernando Nardi

Associate Professor

University for Foreigners Perugia

ITALY

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Ryan R. Morrison

Assistant Professor

Colorado State University

UNITED STATES

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Ellen D'Amico

UNITED STATES

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Qiusheng Wu

Associate Professor

University of Tennessee

UNITED STATES

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Emmanouil N. Anagnostou

University of Connecticut

UNITED STATES

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Tadd Bindas

Product Engineering Professional

UNITED STATES

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Adnan Rajib

Research Interests

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About

Recent Grants

Articles (25)

Small Reservoirs Offer a New Perspective on Flood Reduction in Large Basins

HydroFlame: A Cloud-Native Geospatial Platform for Assessing Post-Fire Hydrological Impacts on HPC Infrastructure

Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios

Remotely Sensed Surface Water Storage Shows Distinct Patterns From SWAT‐Simulated Data

The HAND of flood mapping: multi-dimensional evaluation across data-rich and data-poor basins using existing maps, ground observations, and remote sensing data

Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models

Making earth observations model-friendly: An interoperable tool enabling comprehensive use of earth observations for hydrologic model evaluations

Barriers to quantifying human alterations of global floodplains and how we can overcome them

Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities

Developing an Interactive Online Platform for Advanced Cyber Training and Adaptive Learning Paths

A call for consistency and integration in global surface water estimates

Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors

Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network

Evaluating topography-based approaches for fast floodplain mapping in data-scarce complex-terrain regions: Findings from a Himalayan basin

Mapping global non-floodplain wetlands

Human alterations of the global floodplains 1992–2019

Cyber-enabled autocalibration of hydrologic models to support Open Science

Avert Bangladesh’s looming water crisis through open science and better data

Wetland restoration yields dynamic nitrate responses across the Upper Mississippi river basin

The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset

Improving global flood and drought predictions: integrating non-floodplain wetlands into watershed hydrologic models

Direct Integration of Numerous Dams and Reservoirs Outflow in Continental Scale Hydrologic Modeling

Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling

Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Collaborators (9)

Virginia Smith

Shih-Chieh Kao

Sagy Cohen

Fernando Nardi

Ryan R. Morrison

Ellen D'Amico

Qiusheng Wu

Emmanouil N. Anagnostou

Tadd Bindas

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