Tadd Bindas

498 total citations
8 papers, 58 citations indexed

About

Tadd Bindas is a scholar working on Water Science and Technology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Tadd Bindas has authored 8 papers receiving a total of 58 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Water Science and Technology, 6 papers in Global and Planetary Change and 4 papers in Environmental Engineering. Recurrent topics in Tadd Bindas's work include Hydrology and Watershed Management Studies (7 papers), Flood Risk Assessment and Management (6 papers) and Hydrological Forecasting Using AI (4 papers). Tadd Bindas is often cited by papers focused on Hydrology and Watershed Management Studies (7 papers), Flood Risk Assessment and Management (6 papers) and Hydrological Forecasting Using AI (4 papers). Tadd Bindas collaborates with scholars based in United States, Saudi Arabia and Taiwan. Tadd Bindas's co-authors include Chaopeng Shen, Kathryn Lawson, Farshid Rahmani, Dapeng Feng, Wen‐Ping Tsai, Jiangtao Liu, Yuchen Bian, Emmanouil N. Anagnostou, Xiaofeng Liu and Elizabeth W. Boyer and has published in prestigious journals such as Nature Communications, Water Resources Research and Journal of Hydrology.

In The Last Decade

Tadd Bindas

6 papers receiving 56 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tadd Bindas United States	4	45	37	34	11	4	8	58
Louise Parry United Kingdom	2	62 1.4×	29 0.8×	51 1.5×	10 0.9×	4 1.0×	2	77
Konstantinos Vantas Greece	4	34 0.8×	14 0.4×	18 0.5×	24 2.2×	3 0.8×	10	57
L. Zhong China	3	48 1.1×	37 1.0×	37 1.1×	4 0.4×	1 0.3×	8	66
Judith Ramos Mexico	4	22 0.5×	12 0.3×	47 1.4×	23 2.1×	5 1.3×	6	65
Alberto Guzman United States	2	29 0.6×	14 0.4×	86 2.5×	21 1.9×	8 2.0×	2	101
Michał Wasilewicz Poland	5	38 0.8×	7 0.2×	21 0.6×	17 1.5×	2 0.5×	17	53
Gheorghe Stăncălie Romania	5	24 0.5×	14 0.4×	42 1.2×	14 1.3×	1 0.3×	13	82
Tommaso Abrate Switzerland	4	36 0.8×	7 0.2×	28 0.8×	7 0.6×	1 0.3×	4	58
Keyu Chen China	4	25 0.6×	14 0.4×	26 0.8×	6 0.5×	1 0.3×	10	45

Countries citing papers authored by Tadd Bindas

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Tadd Bindas

This figure shows the co-authorship network connecting the top 25 collaborators of Tadd Bindas. A scholar is included among the top collaborators of Tadd Bindas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tadd Bindas. Tadd Bindas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

1.

Liu, Jiangtao, Chaopeng Shen, Fearghal O’Donncha, et al.. (2025). From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction. Hydrology and earth system sciences. 29(23). 6811–6828.

2.

Song, Yalan, Tadd Bindas, Chaopeng Shen, et al.. (2025). Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications. 16(1). 9169–9169.

3.

Frame, Jonathan, et al.. (2025). Machine Learning for a Heterogeneous Water Modeling Framework. JAWRA Journal of the American Water Resources Association. 61(1). 5 indexed citations

4.

Song, Yalan, Farshid Rahmani, Wei Zhi, et al.. (2024). Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations. Journal of Hydrology. 639. 131573–131573. 9 indexed citations

5.

Bindas, Tadd, Wen‐Ping Tsai, Jiangtao Liu, et al.. (2024). Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning. Water Resources Research. 60(1). 33 indexed citations

6.

Shen, Chaopeng, Sagy Cohen, Virginia Smith, et al.. (2024). Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors. Earth s Future. 12(7). 7 indexed citations

7.

Song, Yalan, Farshid Rahmani, Wei Zhi, et al.. (2023). Deep Learning Insights into Suspended Sediment Concentrations Across the Conterminous United States: Strengths and Limitations. SSRN Electronic Journal. 2 indexed citations

8.

Bindas, Tadd, et al.. (2020). Routing flood waves through the river network utilizing physics-guided machine learning and the Muskingum-Cunge Method. AGU Fall Meeting Abstracts. 2020. 2 indexed citations

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.

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