Tadd Bindas
Impact in
- Water Science and Technology top 5%
- Hydrology and Watershed Management Studies
- Environmental Engineering top 10%
- Hydrological Forecasting Using AI
- Soil Moisture and Remote Sensing
- Groundwater flow and contamination studies
Papers in
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- Hydrology and Watershed Management Studies 6
-
- Hydrological Forecasting Using AI 5
- Co-authors
- Kathryn Elizabeth Lawson (5 shared papers)Chaopeng Shen (5 shared papers)Farshid Rahmani (3 shared papers)Dapeng Feng (1 shared paper)Yuchen Bian (1 shared paper)Wen‐Ping Tsai (1 shared paper)Jiangtao Liu (3 shared papers)Elizabeth W. Boyer (1 shared paper)
- Journals
- Water Resources Research (2 papers)Nature Communications (1 paper)Hydrology and earth system sciences (1 paper)Journal of Hydrology (1 paper)Earth s Future (1 paper)
- Partner nations
- United StatesSaudi ArabiaIreland
In The Last Decade
Tadd Bindas
8 papers receiving 368 citations
Tadd Bindas's Hit Papers
Peers
Comparison fields: 5 of 46
- Water Science and Technology 171
- Environmental Engineering 151
- Global and Planetary Change 139
- Atmospheric Science 55
- Statistical and Nonlinear Physics 23
Countries citing papers authored by Tadd Bindas
This map shows the geographic impact of Tadd Bindas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tadd Bindas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tadd Bindas more than expected).
Fields of papers citing papers by Tadd Bindas
This network shows the impact of papers produced by Tadd Bindas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tadd Bindas. The network helps show where Tadd Bindas may publish in the future.
Co-authors
The 24 scholars most cited alongside Tadd Bindas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Differentiable modelling to unify machine learning and physical models for geosciences Hit paper breakdown → | 2023 | 264 |
| 2 | 2024 | 52 | |
| 3 | 2025 | 22 | |
| 4 | 2024 | 17 | |
| 5 | 2024 | 9 | |
| 6 | 2025 | 5 | |
| 7 | 2025 | 3 | |
| 8 | 2025 | 3 |
About Tadd Bindas
Tadd Bindas is a scholar working on Water Science and Technology, Environmental Engineering, Global and Planetary Change, Ecology and Geophysics, having authored 8 papers that have together received 375 indexed citations. Recurring topics across this work include Flood Risk Assessment and Management (6 papers), Hydrology and Watershed Management Studies (6 papers), Hydrological Forecasting Using AI (5 papers), Hydrology and Sediment Transport Processes (2 papers), Reservoir Engineering and Simulation Methods (1 paper) and Seismic Imaging and Inversion Techniques (1 paper). The work is most often cited by research in Water Science and Technology (171 citations), Environmental Engineering (151 citations), Global and Planetary Change (139 citations), Atmospheric Science (55 citations) and Statistical and Nonlinear Physics (23 citations). Tadd Bindas has collaborated with scholars based in United States, Saudi Arabia and Ireland. Frequent co-authors include Kathryn Elizabeth Lawson, Chaopeng Shen, Farshid Rahmani, Dapeng Feng, Yuchen Bian, Wen‐Ping Tsai, Jiangtao Liu, Elizabeth W. Boyer, Yalan Song and Li Li. Their work appears in journals such as Water Resources Research, Nature Communications, Hydrology and earth system sciences, Journal of Hydrology and Earth s Future.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.