Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Effective and efficient global optimization for conceptual rainfall‐runoff models
19922.7k citationsQingyun Duan, Soroosh Sorooshian et al.profile →
A modified soil adjusted vegetation index
19942.5k citationsJiaguo Qi, Abdelghani Chehbouni et al.Remote Sensing of Environmentprofile →
Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration
19991.7k citationsHoshin V. Gupta, Soroosh Sorooshian et al.profile →
A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons
20171.4k citationsChiyuan Miao, Qingyun Duan et al.profile →
Shuffled complex evolution approach for effective and efficient global minimization
19931.3k citationsQingyun Duan, Soroosh Sorooshian et al.profile →
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
19951.2k citationsKuolin Hsu, Hoshin V. Gupta et al.profile →
Optimal use of the SCE-UA global optimization method for calibrating watershed models
19941.1k citationsQingyun Duan, Soroosh Sorooshian et al.profile →
Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
19981.1k citationsHoshin V. Gupta, Soroosh Sorooshian et al.profile →
PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies
20141.1k citationsKuolin Hsu, Soroosh Sorooshian et al.profile →
Evaluation of PERSIANN System Satellite–Based Estimates of Tropical Rainfall
20001.0k citationsSoroosh Sorooshian, Kuolin Hsu et al.profile →
A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters
2003988 citationsJasper A. Vrugt, Hoshin V. Gupta et al.profile →
Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks
1997893 citationsKuolin Hsu, Xiaogang Gao et al.Journal of Applied Meteorologyprofile →
Multi-objective global optimization for hydrologic models
1998716 citationsHoshin V. Gupta, Soroosh Sorooshian et al.profile →
Dual state–parameter estimation of hydrological models using ensemble Kalman filter
2004715 citationsSoroosh Sorooshian, Hoshin V. Gupta et al.profile →
Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System
2004673 citationsKuolin Hsu, Soroosh Sorooshian et al.Journal of Applied Meteorologyprofile →
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter
2005624 citationsKuolin Hsu, Hoshin V. Gupta et al.profile →
Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops
2005536 citationsQingyun Duan, Hoshin V. Gupta et al.profile →
Multi-model ensemble hydrologic prediction using Bayesian model averaging
2007533 citationsQingyun Duan, Xiaogang Gao et al.profile →
Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods
2000533 citationsHoshin V. Gupta, Soroosh Sorooshian et al.profile →
Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data
1996510 citationsHoshin V. Gupta, Soroosh Sorooshian et al.profile →
Effective and efficient algorithm for multiobjective optimization of hydrologic models
2003505 citationsJasper A. Vrugt, Hoshin V. Gupta et al.profile →
Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
2018282 citationsTiantian Yang, Soroosh Sorooshian et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Soroosh Sorooshian
Since
Specialization
Citations
This map shows the geographic impact of Soroosh Sorooshian'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 Soroosh Sorooshian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soroosh Sorooshian more than expected).
Fields of papers citing papers by Soroosh Sorooshian
This network shows the impact of papers produced by Soroosh Sorooshian. 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 Soroosh Sorooshian. The network helps show where Soroosh Sorooshian may publish in the future.
Co-authorship network of co-authors of Soroosh Sorooshian
This figure shows the co-authorship network connecting the top 25 collaborators of Soroosh Sorooshian.
A scholar is included among the top collaborators of Soroosh Sorooshian 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 Soroosh Sorooshian. Soroosh Sorooshian is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nguyen, Phu, E. J. Shearer, Hien Tran, et al.. (2018). The CHRS Data Portal for Distributing PERSIANN Family Global Satellite Precipitation Data. AGUFM. 2018.1 indexed citations
6.
Sorooshian, Soroosh, et al.. (2012). Interrelation Study of Entrepreneur's Capability. SSRN Electronic Journal.8 indexed citations
7.
Huffman, George J., David T. Bolvin, Dan Braithwaite, et al.. (2011). The Day-1 GPM Combined Precipitation Algorithm: IMERG. AGU Fall Meeting Abstracts. 2011.1 indexed citations
8.
Gourley, Jonathan J., et al.. (2010). Pixel-Based Very Short-Term Precipitation Forecasting for Hydrological Application. AGUFM. 2010.2 indexed citations
9.
Sorooshian, Soroosh, et al.. (2010). Structural modeling of entrepreneur's effectiveness. Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia).5 indexed citations
10.
Sorooshian, Soroosh. (2008). Hydrological modelling and the water cycle : coupling the atmospheric and hydrological models. Springer eBooks.28 indexed citations
11.
Jin, Jiming, Norman L. Miller, Soroosh Sorooshian, & Xiaogang Gao. (2004). Relationship Between Atmospheric circulation and Snowpack in theWestern United States. Hydrological Processes. 20(4).3 indexed citations
12.
Pagano, Thomas C., et al.. (2003). Review of Middle Eastern hydroclimatology and seasonal teleconnections. eScholarship (California Digital Library).6 indexed citations
13.
Vrugt, Jasper A., Hoshin V. Gupta, Willem Bouten, & Soroosh Sorooshian. (2002). A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrological model parameters. AGU Fall Meeting Abstracts. 2002.23 indexed citations
14.
Sorooshian, Soroosh, et al.. (1999). A multi-step automatic calibration scheme (MACS) for river forecasting models utilizing the national weather service river forecast system (NWSRFS). UA Campus Repository (The University of Arizona).3 indexed citations
15.
Pagano, Thomas C., Holly C. Hartmann, Soroosh Sorooshian, & Roger C. Bales. (1999). Advances in seasonal forecasting for water management in Arizona: a case study of the 1997-98 El Niño. UA Campus Repository (The University of Arizona).9 indexed citations
16.
Hsu, Kuolin, Hoshin V. Gupta, & Soroosh Sorooshian. (1998). Streamflow Forecasting Using Artificial Neural Networks. Water resources engineering. 967–972.21 indexed citations
17.
Hsu, Kuolin, Xiaogang Gao, Soroosh Sorooshian, & Hoshin V. Gupta. (1997). Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks. Journal of Applied Meteorology. 36(9). 1176–1190.893 indexed citations breakdown →
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.